Overview

Brought to you by YData

Dataset statistics

Number of variables49
Number of observations163864
Missing cells710419
Missing cells (%)8.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory61.3 MiB
Average record size in memory392.0 B

Variable types

Text7
DateTime1
Numeric23
Categorical18

Alerts

condition has constant value "Не требует ремонта" Constant
acceleration is highly overall correlated with car_price and 8 other fieldsHigh correlation
car_price is highly overall correlated with acceleration and 9 other fieldsHigh correlation
class_auto is highly overall correlated with drive and 2 other fieldsHigh correlation
clearence_max is highly overall correlated with heightHigh correlation
curb_weight is highly overall correlated with acceleration and 14 other fieldsHigh correlation
customs is highly overall correlated with original_pts and 1 other fieldsHigh correlation
cyl_count is highly overall correlated with acceleration and 8 other fieldsHigh correlation
drive is highly overall correlated with class_auto and 2 other fieldsHigh correlation
eng_power is highly overall correlated with acceleration and 11 other fieldsHigh correlation
eng_size is highly overall correlated with car_price and 10 other fieldsHigh correlation
eng_type is highly overall correlated with fuel_brand and 1 other fieldsHigh correlation
engine_loc1 is highly overall correlated with engine_loc2 and 1 other fieldsHigh correlation
engine_loc2 is highly overall correlated with class_auto and 7 other fieldsHigh correlation
front_brakes is highly overall correlated with fuel_brand and 3 other fieldsHigh correlation
fuel_brand is highly overall correlated with eng_type and 1 other fieldsHigh correlation
fuel_cons is highly overall correlated with curb_weight and 4 other fieldsHigh correlation
gross_weight is highly overall correlated with acceleration and 14 other fieldsHigh correlation
height is highly overall correlated with clearence_max and 3 other fieldsHigh correlation
long is highly overall correlated with acceleration and 12 other fieldsHigh correlation
max_speed is highly overall correlated with acceleration and 9 other fieldsHigh correlation
max_torq is highly overall correlated with acceleration and 11 other fieldsHigh correlation
mileage is highly overall correlated with pow_resrv and 1 other fieldsHigh correlation
original_pts is highly overall correlated with customsHigh correlation
pow_resrv is highly overall correlated with class_auto and 15 other fieldsHigh correlation
rear_brakes is highly overall correlated with curb_weight and 5 other fieldsHigh correlation
seat_count_max is highly overall correlated with seat_count_minHigh correlation
seat_count_min is highly overall correlated with pow_resrv and 1 other fieldsHigh correlation
st_wheel is highly overall correlated with pow_resrvHigh correlation
state_mark is highly overall correlated with pow_resrv and 1 other fieldsHigh correlation
turbocharg is highly overall correlated with pow_resrvHigh correlation
v_bag_max is highly overall correlated with v_bag_minHigh correlation
v_bag_min is highly overall correlated with gross_weight and 2 other fieldsHigh correlation
v_tank is highly overall correlated with curb_weight and 9 other fieldsHigh correlation
width is highly overall correlated with acceleration and 11 other fieldsHigh correlation
year is highly overall correlated with car_price and 3 other fieldsHigh correlation
avail is highly imbalanced (83.2%) Imbalance
eng_type is highly imbalanced (74.9%) Imbalance
st_wheel is highly imbalanced (80.2%) Imbalance
customs is highly imbalanced (99.9%) Imbalance
front_brakes is highly imbalanced (93.9%) Imbalance
engine_loc1 is highly imbalanced (96.2%) Imbalance
eng_size has 1818 (1.1%) missing values Missing
pow_resrv has 162046 (98.9%) missing values Missing
v_tank has 53386 (32.6%) missing values Missing
curb_weight has 3672 (2.2%) missing values Missing
gross_weight has 15621 (9.5%) missing values Missing
rear_brakes has 1884 (1.1%) missing values Missing
max_speed has 6198 (3.8%) missing values Missing
acceleration has 58350 (35.6%) missing values Missing
fuel_cons has 65200 (39.8%) missing values Missing
fuel_brand has 2420 (1.5%) missing values Missing
engine_loc1 has 1939 (1.2%) missing values Missing
engine_loc2 has 2939 (1.8%) missing values Missing
turbocharg has 1891 (1.2%) missing values Missing
max_torq has 50819 (31.0%) missing values Missing
cyl_count has 1818 (1.1%) missing values Missing
seat_count_min has 158775 (96.9%) missing values Missing
clearence_max has 3177 (1.9%) missing values Missing
v_bag_max has 58564 (35.7%) missing values Missing
v_bag_min has 58564 (35.7%) missing values Missing
car_price is highly skewed (γ1 = 35.99555867) Skewed
max_torq is highly skewed (γ1 = 31.57520195) Skewed

Reproduction

Analysis started2024-12-29 21:10:05.803125
Analysis finished2024-12-29 21:11:21.839222
Duration1 minute and 16.04 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Distinct215
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:22.797093image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length25
Median length19
Mean length7.0447871
Min length2

Characters and Unicode

Total characters1154387
Distinct characters84
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)< 0.1%

Sample

1st rowAbarth
2nd rowAbarth
3rd rowAbarth
4th rowAbarth
5th rowAbarth
ValueCountFrequency (%)
lada 32366
 
16.1%
ваз 32366
 
16.1%
chevrolet 12811
 
6.4%
bmw 7039
 
3.5%
renault 4881
 
2.4%
skoda 4855
 
2.4%
toyota 4646
 
2.3%
volkswagen 4255
 
2.1%
audi 4069
 
2.0%
chery 4062
 
2.0%
Other values (225) 89961
44.7%
2024-12-30T00:11:23.109376image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 124756
 
10.8%
e 89274
 
7.7%
o 67159
 
5.8%
d 55981
 
4.8%
L 41729
 
3.6%
З 38583
 
3.3%
А 37899
 
3.3%
n 37713
 
3.3%
37447
 
3.2%
l 37086
 
3.2%
Other values (74) 586760
50.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1154387
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 124756
 
10.8%
e 89274
 
7.7%
o 67159
 
5.8%
d 55981
 
4.8%
L 41729
 
3.6%
З 38583
 
3.3%
А 37899
 
3.3%
n 37713
 
3.3%
37447
 
3.2%
l 37086
 
3.2%
Other values (74) 586760
50.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1154387
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 124756
 
10.8%
e 89274
 
7.7%
o 67159
 
5.8%
d 55981
 
4.8%
L 41729
 
3.6%
З 38583
 
3.3%
А 37899
 
3.3%
n 37713
 
3.3%
37447
 
3.2%
l 37086
 
3.2%
Other values (74) 586760
50.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1154387
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 124756
 
10.8%
e 89274
 
7.7%
o 67159
 
5.8%
d 55981
 
4.8%
L 41729
 
3.6%
З 38583
 
3.3%
А 37899
 
3.3%
n 37713
 
3.3%
37447
 
3.2%
l 37086
 
3.2%
Other values (74) 586760
50.8%
Distinct1985
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:23.332580image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length32
Median length22
Mean length5.7635539
Min length1

Characters and Unicode

Total characters944439
Distinct characters115
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique375 ?
Unique (%)0.2%

Sample

1st row500
2nd row500
3rd row500
4th row500
5th row500
ValueCountFrequency (%)
granta 4434
 
2.3%
2114 4144
 
2.1%
серии 3669
 
1.9%
4x4 3497
 
1.8%
niva 3102
 
1.6%
tiggo 3026
 
1.6%
2121 2982
 
1.5%
2110 2563
 
1.3%
2112 2249
 
1.2%
3 2215
 
1.1%
Other values (1829) 161889
83.5%
2024-12-30T00:11:23.647866image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 86163
 
9.1%
r 57576
 
6.1%
e 51696
 
5.5%
o 47745
 
5.1%
1 45459
 
4.8%
n 38624
 
4.1%
t 38317
 
4.1%
i 37657
 
4.0%
29906
 
3.2%
2 28856
 
3.1%
Other values (105) 482440
51.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 944439
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 86163
 
9.1%
r 57576
 
6.1%
e 51696
 
5.5%
o 47745
 
5.1%
1 45459
 
4.8%
n 38624
 
4.1%
t 38317
 
4.1%
i 37657
 
4.0%
29906
 
3.2%
2 28856
 
3.1%
Other values (105) 482440
51.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 944439
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 86163
 
9.1%
r 57576
 
6.1%
e 51696
 
5.5%
o 47745
 
5.1%
1 45459
 
4.8%
n 38624
 
4.1%
t 38317
 
4.1%
i 37657
 
4.0%
29906
 
3.2%
2 28856
 
3.1%
Other values (105) 482440
51.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 944439
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 86163
 
9.1%
r 57576
 
6.1%
e 51696
 
5.5%
o 47745
 
5.1%
1 45459
 
4.8%
n 38624
 
4.1%
t 38317
 
4.1%
i 37657
 
4.0%
29906
 
3.2%
2 28856
 
3.1%
Other values (105) 482440
51.1%
Distinct1280
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:23.896092image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length34
Median length31
Mean length7.727103
Min length1

Characters and Unicode

Total characters1266194
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique241 ?
Unique (%)0.1%

Sample

1st rowI Рестайлинг
2nd rowI
3rd rowI Рестайлинг
4th rowI
5th rowI Рестайлинг
ValueCountFrequency (%)
i 70539
28.2%
рестайлинг 52143
20.8%
ii 25051
 
10.0%
iii 13847
 
5.5%
iv 7792
 
3.1%
v 4359
 
1.7%
2001-2013 4144
 
1.7%
2 3167
 
1.3%
1995-2014 2563
 
1.0%
vi 2542
 
1.0%
Other values (896) 64072
25.6%
2024-12-30T00:11:24.248412image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 179237
 
14.2%
86355
 
6.8%
0 68787
 
5.4%
2 65307
 
5.2%
1 56581
 
4.5%
н 56135
 
4.4%
е 52147
 
4.1%
л 52146
 
4.1%
т 52146
 
4.1%
и 52146
 
4.1%
Other values (71) 545207
43.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1266194
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 179237
 
14.2%
86355
 
6.8%
0 68787
 
5.4%
2 65307
 
5.2%
1 56581
 
4.5%
н 56135
 
4.4%
е 52147
 
4.1%
л 52146
 
4.1%
т 52146
 
4.1%
и 52146
 
4.1%
Other values (71) 545207
43.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1266194
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 179237
 
14.2%
86355
 
6.8%
0 68787
 
5.4%
2 65307
 
5.2%
1 56581
 
4.5%
н 56135
 
4.4%
е 52147
 
4.1%
л 52146
 
4.1%
т 52146
 
4.1%
и 52146
 
4.1%
Other values (71) 545207
43.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1266194
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 179237
 
14.2%
86355
 
6.8%
0 68787
 
5.4%
2 65307
 
5.2%
1 56581
 
4.5%
н 56135
 
4.4%
е 52147
 
4.1%
л 52146
 
4.1%
т 52146
 
4.1%
и 52146
 
4.1%
Other values (71) 545207
43.1%
Distinct289
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:24.503644image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length37
Median length36
Mean length12.19258
Min length4

Characters and Unicode

Total characters1997925
Distinct characters109
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)< 0.1%

Sample

1st rowКабриолет
2nd rowКабриолет
3rd rowКабриолет
4th rowХэтчбек 3 дв.
5th rowХэтчбек 3 дв.
ValueCountFrequency (%)
дв 98560
26.3%
5 91417
24.4%
внедорожник 64162
17.1%
седан 47858
12.8%
хэтчбек 26291
 
7.0%
универсал 8216
 
2.2%
3 7002
 
1.9%
лифтбек 6924
 
1.8%
минивэн 2387
 
0.6%
компактвэн 2324
 
0.6%
Other values (217) 19451
 
5.2%
2024-12-30T00:11:24.820932image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
д 211132
 
10.6%
210728
 
10.5%
н 196356
 
9.8%
е 156243
 
7.8%
о 134725
 
6.7%
в 113700
 
5.7%
к 104091
 
5.2%
. 98560
 
4.9%
5 91456
 
4.6%
и 88550
 
4.4%
Other values (99) 592384
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1997925
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
д 211132
 
10.6%
210728
 
10.5%
н 196356
 
9.8%
е 156243
 
7.8%
о 134725
 
6.7%
в 113700
 
5.7%
к 104091
 
5.2%
. 98560
 
4.9%
5 91456
 
4.6%
и 88550
 
4.4%
Other values (99) 592384
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1997925
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
д 211132
 
10.6%
210728
 
10.5%
н 196356
 
9.8%
е 156243
 
7.8%
о 134725
 
6.7%
в 113700
 
5.7%
к 104091
 
5.2%
. 98560
 
4.9%
5 91456
 
4.6%
и 88550
 
4.4%
Other values (99) 592384
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1997925
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
д 211132
 
10.6%
210728
 
10.5%
н 196356
 
9.8%
е 156243
 
7.8%
о 134725
 
6.7%
в 113700
 
5.7%
к 104091
 
5.2%
. 98560
 
4.9%
5 91456
 
4.6%
и 88550
 
4.4%
Other values (99) 592384
29.6%
Distinct6559
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:25.042133image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length53
Median length51
Mean length19.837329
Min length16

Characters and Unicode

Total characters3250624
Distinct characters108
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1959 ?
Unique (%)1.2%

Sample

1st row1.4 MT (180 л.с.)
2nd row1.4 MT (135 л.с.)
3rd row1.4 AMT (180 л.с.)
4th row1.4 MT (135 л.с.)
5th row1.4 AT (159 л.с.)
ValueCountFrequency (%)
л.с 162046
21.4%
mt 73659
 
9.7%
at 62727
 
8.3%
4wd 60291
 
8.0%
1.6 42202
 
5.6%
2.0 21420
 
2.8%
1.5 20462
 
2.7%
amt 14562
 
1.9%
cvt 12988
 
1.7%
150 9502
 
1.3%
Other values (1520) 277294
36.6%
2024-12-30T00:11:25.373434image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
593289
18.3%
. 487724
15.0%
1 205180
 
6.3%
T 166628
 
5.1%
) 164151
 
5.0%
( 164151
 
5.0%
л 162189
 
5.0%
с 162144
 
5.0%
0 126116
 
3.9%
4 116723
 
3.6%
Other values (98) 902329
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3250624
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
593289
18.3%
. 487724
15.0%
1 205180
 
6.3%
T 166628
 
5.1%
) 164151
 
5.0%
( 164151
 
5.0%
л 162189
 
5.0%
с 162144
 
5.0%
0 126116
 
3.9%
4 116723
 
3.6%
Other values (98) 902329
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3250624
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
593289
18.3%
. 487724
15.0%
1 205180
 
6.3%
T 166628
 
5.1%
) 164151
 
5.0%
( 164151
 
5.0%
л 162189
 
5.0%
с 162144
 
5.0%
0 126116
 
3.9%
4 116723
 
3.6%
Other values (98) 902329
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3250624
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
593289
18.3%
. 487724
15.0%
1 205180
 
6.3%
T 166628
 
5.1%
) 164151
 
5.0%
( 164151
 
5.0%
л 162189
 
5.0%
с 162144
 
5.0%
0 126116
 
3.9%
4 116723
 
3.6%
Other values (98) 902329
27.8%
Distinct366
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Minimum2024-01-01 00:00:00
Maximum2024-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2024-12-30T00:11:25.466519image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:25.558603image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

car_price
Real number (ℝ)

High correlation  Skewed 

Distinct12331
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2113057.9
Minimum10500
Maximum5.5 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:25.651687image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum10500
5-th percentile149000
Q1470000
median1000000
Q32100000
95-th percentile6900000
Maximum5.5 × 108
Range5.499895 × 108
Interquartile range (IQR)1630000

Descriptive statistics

Standard deviation5410261.7
Coefficient of variation (CV)2.5603944
Kurtosis2707.988
Mean2113057.9
Median Absolute Deviation (MAD)680000
Skewness35.995559
Sum3.4625412 × 1011
Variance2.9270932 × 1013
MonotonicityNot monotonic
2024-12-30T00:11:25.742770image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
250000 1567
 
1.0%
350000 1466
 
0.9%
650000 1465
 
0.9%
850000 1407
 
0.9%
750000 1354
 
0.8%
150000 1351
 
0.8%
200000 1290
 
0.8%
1100000 1272
 
0.8%
450000 1267
 
0.8%
800000 1247
 
0.8%
Other values (12321) 150178
91.6%
ValueCountFrequency (%)
10500 1
 
< 0.1%
16000 1
 
< 0.1%
25000 2
 
< 0.1%
26000 1
 
< 0.1%
27000 1
 
< 0.1%
28000 2
 
< 0.1%
29000 1
 
< 0.1%
30000 5
< 0.1%
32000 3
< 0.1%
33000 1
 
< 0.1%
ValueCountFrequency (%)
550000000 1
< 0.1%
516000000 1
< 0.1%
500370240 1
< 0.1%
426615810 1
< 0.1%
420000000 1
< 0.1%
400000000 2
< 0.1%
378670240 1
< 0.1%
307000000 1
< 0.1%
290000000 1
< 0.1%
187000000 1
< 0.1%
Distinct4773
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:25.928939image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length48
Median length37
Mean length8.5623017
Min length2

Characters and Unicode

Total characters1403053
Distinct characters78
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2088 ?
Unique (%)1.3%

Sample

1st rowОбнинск
2nd rowМинск
3rd rowСанкт-Петербург
4th rowМосква
5th rowМосква
ValueCountFrequency (%)
москва 26055
 
15.1%
санкт-петербург 9302
 
5.4%
владивосток 5061
 
2.9%
екатеринбург 4008
 
2.3%
краснодар 3966
 
2.3%
казань 3464
 
2.0%
нижний 2997
 
1.7%
новгород 2944
 
1.7%
воронеж 2903
 
1.7%
новосибирск 2751
 
1.6%
Other values (4722) 108789
63.2%
2024-12-30T00:11:26.210194image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
о 140945
 
10.0%
а 136254
 
9.7%
к 100440
 
7.2%
р 98856
 
7.0%
с 95303
 
6.8%
н 77782
 
5.5%
е 74330
 
5.3%
в 70766
 
5.0%
и 57428
 
4.1%
т 49590
 
3.5%
Other values (68) 501359
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1403053
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
о 140945
 
10.0%
а 136254
 
9.7%
к 100440
 
7.2%
р 98856
 
7.0%
с 95303
 
6.8%
н 77782
 
5.5%
е 74330
 
5.3%
в 70766
 
5.0%
и 57428
 
4.1%
т 49590
 
3.5%
Other values (68) 501359
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1403053
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
о 140945
 
10.0%
а 136254
 
9.7%
к 100440
 
7.2%
р 98856
 
7.0%
с 95303
 
6.8%
н 77782
 
5.5%
е 74330
 
5.3%
в 70766
 
5.0%
и 57428
 
4.1%
т 49590
 
3.5%
Other values (68) 501359
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1403053
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
о 140945
 
10.0%
а 136254
 
9.7%
к 100440
 
7.2%
р 98856
 
7.0%
с 95303
 
6.8%
н 77782
 
5.5%
е 74330
 
5.3%
в 70766
 
5.0%
и 57428
 
4.1%
т 49590
 
3.5%
Other values (68) 501359
35.7%

avail
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
В наличии
156549 
На заказ
 
7252
В пути
 
63

Length

Max length9
Median length9
Mean length8.9545904
Min length6

Characters and Unicode

Total characters1467335
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowВ наличии
2nd rowВ наличии
3rd rowВ наличии
4th rowВ наличии
5th rowВ наличии

Common Values

ValueCountFrequency (%)
В наличии 156549
95.5%
На заказ 7252
 
4.4%
В пути 63
 
< 0.1%

Length

2024-12-30T00:11:26.305281image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T00:11:26.386354image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
в 156612
47.8%
наличии 156549
47.8%
на 7252
 
2.2%
заказ 7252
 
2.2%
пути 63
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
и 469710
32.0%
а 178305
 
12.2%
163864
 
11.2%
В 156612
 
10.7%
н 156549
 
10.7%
л 156549
 
10.7%
ч 156549
 
10.7%
з 14504
 
1.0%
Н 7252
 
0.5%
к 7252
 
0.5%
Other values (3) 189
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1467335
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
и 469710
32.0%
а 178305
 
12.2%
163864
 
11.2%
В 156612
 
10.7%
н 156549
 
10.7%
л 156549
 
10.7%
ч 156549
 
10.7%
з 14504
 
1.0%
Н 7252
 
0.5%
к 7252
 
0.5%
Other values (3) 189
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1467335
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
и 469710
32.0%
а 178305
 
12.2%
163864
 
11.2%
В 156612
 
10.7%
н 156549
 
10.7%
л 156549
 
10.7%
ч 156549
 
10.7%
з 14504
 
1.0%
Н 7252
 
0.5%
к 7252
 
0.5%
Other values (3) 189
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1467335
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
и 469710
32.0%
а 178305
 
12.2%
163864
 
11.2%
В 156612
 
10.7%
н 156549
 
10.7%
л 156549
 
10.7%
ч 156549
 
10.7%
з 14504
 
1.0%
Н 7252
 
0.5%
к 7252
 
0.5%
Other values (3) 189
 
< 0.1%

year
Real number (ℝ)

High correlation 

Distinct92
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.221
Minimum1923
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:26.470431image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1923
5-th percentile1999
Q12008
median2013
Q32019
95-th percentile2023
Maximum2024
Range101
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.597091
Coefficient of variation (CV)0.0042724387
Kurtosis7.9789514
Mean2012.221
Median Absolute Deviation (MAD)5
Skewness-1.8699777
Sum3.2973059 × 108
Variance73.909974
MonotonicityNot monotonic
2024-12-30T00:11:26.565517image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2012 11235
 
6.9%
2011 10005
 
6.1%
2021 9918
 
6.1%
2013 9560
 
5.8%
2008 9553
 
5.8%
2007 8228
 
5.0%
2019 8089
 
4.9%
2014 7750
 
4.7%
2020 7731
 
4.7%
2023 7728
 
4.7%
Other values (82) 74067
45.2%
ValueCountFrequency (%)
1923 1
 
< 0.1%
1932 1
 
< 0.1%
1935 2
 
< 0.1%
1936 2
 
< 0.1%
1937 5
< 0.1%
1938 4
< 0.1%
1939 5
< 0.1%
1940 1
 
< 0.1%
1941 5
< 0.1%
1942 4
< 0.1%
ValueCountFrequency (%)
2024 4413
2.7%
2023 7728
4.7%
2022 4622
2.8%
2021 9918
6.1%
2020 7731
4.7%
2019 8089
4.9%
2018 7682
4.7%
2017 6279
3.8%
2016 5112
3.1%
2015 5061
3.1%

mileage
Real number (ℝ)

High correlation 

Distinct46705
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144549.39
Minimum1
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:26.660604image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6000
Q169147
median140000
Q3201677
95-th percentile308000
Maximum1000000
Range999999
Interquartile range (IQR)132530

Descriptive statistics

Standard deviation97012.157
Coefficient of variation (CV)0.67113503
Kurtosis2.5028017
Mean144549.39
Median Absolute Deviation (MAD)67000
Skewness0.87438009
Sum2.3686441 × 1010
Variance9.4113587 × 109
MonotonicityNot monotonic
2024-12-30T00:11:26.758693image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200000 2928
 
1.8%
150000 2040
 
1.2%
250000 1893
 
1.2%
180000 1607
 
1.0%
100000 1541
 
0.9%
300000 1437
 
0.9%
170000 1331
 
0.8%
160000 1249
 
0.8%
190000 1136
 
0.7%
220000 1108
 
0.7%
Other values (46695) 147594
90.1%
ValueCountFrequency (%)
1 359
0.2%
2 31
 
< 0.1%
3 42
 
< 0.1%
4 33
 
< 0.1%
5 202
 
0.1%
6 41
 
< 0.1%
7 51
 
< 0.1%
8 62
 
< 0.1%
9 30
 
< 0.1%
10 605
0.4%
ValueCountFrequency (%)
1000000 7
 
< 0.1%
999999 23
< 0.1%
999990 1
 
< 0.1%
999985 2
 
< 0.1%
998321 1
 
< 0.1%
992588 1
 
< 0.1%
989852 1
 
< 0.1%
966666 1
 
< 0.1%
960000 1
 
< 0.1%
958336 1
 
< 0.1%

color
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
чёрный
35668 
белый
34812 
серый
27014 
серебристый
18015 
синий
14040 
Other values (11)
34315 

Length

Max length11
Median length10
Mean length6.46967
Min length5

Characters and Unicode

Total characters1060146
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowсеребристый
2nd rowчёрный
3rd rowбелый
4th rowсерый
5th rowчёрный

Common Values

ValueCountFrequency (%)
чёрный 35668
21.8%
белый 34812
21.2%
серый 27014
16.5%
серебристый 18015
11.0%
синий 14040
 
8.6%
красный 9161
 
5.6%
зелёный 7575
 
4.6%
коричневый 5783
 
3.5%
бежевый 3524
 
2.2%
голубой 2227
 
1.4%
Other values (6) 6045
 
3.7%

Length

2024-12-30T00:11:26.853779image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
чёрный 35668
21.8%
белый 34812
21.2%
серый 27014
16.5%
серебристый 18015
11.0%
синий 14040
 
8.6%
красный 9161
 
5.6%
зелёный 7575
 
4.6%
коричневый 5783
 
3.5%
бежевый 3524
 
2.2%
голубой 2227
 
1.4%
Other values (6) 6045
 
3.7%

Most occurring characters

ValueCountFrequency (%)
й 163864
15.5%
ы 147597
13.9%
е 120441
11.4%
р 118034
11.1%
с 87391
8.2%
н 74935
7.1%
б 58578
 
5.5%
и 54119
 
5.1%
л 47905
 
4.5%
ё 44293
 
4.2%
Other values (12) 142989
13.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1060146
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
й 163864
15.5%
ы 147597
13.9%
е 120441
11.4%
р 118034
11.1%
с 87391
8.2%
н 74935
7.1%
б 58578
 
5.5%
и 54119
 
5.1%
л 47905
 
4.5%
ё 44293
 
4.2%
Other values (12) 142989
13.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1060146
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
й 163864
15.5%
ы 147597
13.9%
е 120441
11.4%
р 118034
11.1%
с 87391
8.2%
н 74935
7.1%
б 58578
 
5.5%
и 54119
 
5.1%
л 47905
 
4.5%
ё 44293
 
4.2%
Other values (12) 142989
13.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1060146
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
й 163864
15.5%
ы 147597
13.9%
е 120441
11.4%
р 118034
11.1%
с 87391
8.2%
н 74935
7.1%
б 58578
 
5.5%
и 54119
 
5.1%
л 47905
 
4.5%
ё 44293
 
4.2%
Other values (12) 142989
13.5%

eng_size
Real number (ℝ)

High correlation  Missing 

Distinct74
Distinct (%)< 0.1%
Missing1818
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean2.0111055
Minimum0.2
Maximum8.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:26.940858image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile1.3
Q11.6
median1.6
Q32
95-th percentile3.6
Maximum8.3
Range8.1
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.84046286
Coefficient of variation (CV)0.41791088
Kurtosis7.8078814
Mean2.0111055
Median Absolute Deviation (MAD)0.2
Skewness2.4583195
Sum325891.6
Variance0.70637782
MonotonicityNot monotonic
2024-12-30T00:11:27.040949image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.6 42908
26.2%
2 26968
16.5%
1.5 22583
13.8%
3 8759
 
5.3%
2.5 7589
 
4.6%
1.4 7188
 
4.4%
1.8 6658
 
4.1%
1.7 6314
 
3.9%
2.4 4233
 
2.6%
2.7 2658
 
1.6%
Other values (64) 26188
16.0%
ValueCountFrequency (%)
0.2 2
 
< 0.1%
0.4 4
 
< 0.1%
0.5 1
 
< 0.1%
0.6 29
 
< 0.1%
0.7 838
 
0.5%
0.8 1618
1.0%
0.9 78
 
< 0.1%
1 895
 
0.5%
1.1 156
 
0.1%
1.2 2549
1.6%
ValueCountFrequency (%)
8.3 3
 
< 0.1%
8.2 3
 
< 0.1%
8.1 1
 
< 0.1%
8 8
 
< 0.1%
7.7 6
 
< 0.1%
7.6 1
 
< 0.1%
7.5 5
 
< 0.1%
7.4 2
 
< 0.1%
7 6
 
< 0.1%
6.8 234
0.1%

eng_power
Real number (ℝ)

High correlation 

Distinct528
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159.93452
Minimum10
Maximum1500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:27.137037image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile73
Q190
median129
Q3184
95-th percentile379
Maximum1500
Range1490
Interquartile range (IQR)94

Descriptive statistics

Standard deviation106.21331
Coefficient of variation (CV)0.66410498
Kurtosis10.61926
Mean159.93452
Median Absolute Deviation (MAD)42
Skewness2.6675134
Sum26207510
Variance11281.267
MonotonicityNot monotonic
2024-12-30T00:11:27.231122image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 9493
 
5.8%
90 5698
 
3.5%
81 5046
 
3.1%
106 4294
 
2.6%
80 4218
 
2.6%
109 3965
 
2.4%
249 3754
 
2.3%
140 3248
 
2.0%
87 2671
 
1.6%
105 2607
 
1.6%
Other values (518) 118870
72.5%
ValueCountFrequency (%)
10 2
 
< 0.1%
12 3
 
< 0.1%
13 2
 
< 0.1%
19 2
 
< 0.1%
23 7
 
< 0.1%
25 3
 
< 0.1%
26 8
 
< 0.1%
27 28
< 0.1%
29 18
< 0.1%
30 4
 
< 0.1%
ValueCountFrequency (%)
1500 7
 
< 0.1%
1265 11
< 0.1%
1197 18
< 0.1%
1035 8
< 0.1%
1020 10
< 0.1%
1015 4
 
< 0.1%
1014 10
< 0.1%
1000 1
 
< 0.1%
966 2
 
< 0.1%
925 2
 
< 0.1%

eng_type
Categorical

High correlation  Imbalance 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Бензин
139941 
Дизель
17130 
Гибрид
 
2593
Бензин, газобаллонное оборудование
 
2296
Электро
 
1818
Other values (4)
 
86

Length

Max length34
Median length6
Mean length6.4076307
Min length3

Characters and Unicode

Total characters1049980
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowБензин
2nd rowБензин
3rd rowБензин
4th rowБензин
5th rowБензин

Common Values

ValueCountFrequency (%)
Бензин 139941
85.4%
Дизель 17130
 
10.5%
Гибрид 2593
 
1.6%
Бензин, газобаллонное оборудование 2296
 
1.4%
Электро 1818
 
1.1%
Газ 53
 
< 0.1%
Газ, газобаллонное оборудование 25
 
< 0.1%
Дизель, газобаллонное оборудование 6
 
< 0.1%
Гибрид, газобаллонное оборудование 2
 
< 0.1%

Length

2024-12-30T00:11:27.325209image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T00:11:27.413288image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
бензин 142237
84.4%
дизель 17136
 
10.2%
гибрид 2595
 
1.5%
газобаллонное 2329
 
1.4%
оборудование 2329
 
1.4%
электро 1818
 
1.1%
газ 78
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
н 291461
27.8%
и 166892
15.9%
е 165849
15.8%
з 161780
15.4%
Б 142237
13.5%
л 23612
 
2.2%
Д 17136
 
1.6%
ь 17136
 
1.6%
о 15792
 
1.5%
б 7253
 
0.7%
Other values (12) 40832
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1049980
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
н 291461
27.8%
и 166892
15.9%
е 165849
15.8%
з 161780
15.4%
Б 142237
13.5%
л 23612
 
2.2%
Д 17136
 
1.6%
ь 17136
 
1.6%
о 15792
 
1.5%
б 7253
 
0.7%
Other values (12) 40832
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1049980
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
н 291461
27.8%
и 166892
15.9%
е 165849
15.8%
з 161780
15.4%
Б 142237
13.5%
л 23612
 
2.2%
Д 17136
 
1.6%
ь 17136
 
1.6%
о 15792
 
1.5%
б 7253
 
0.7%
Other values (12) 40832
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1049980
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
н 291461
27.8%
и 166892
15.9%
е 165849
15.8%
з 161780
15.4%
Б 142237
13.5%
л 23612
 
2.2%
Д 17136
 
1.6%
ь 17136
 
1.6%
о 15792
 
1.5%
б 7253
 
0.7%
Other values (12) 40832
 
3.9%

pow_resrv
Real number (ℝ)

High correlation  Missing 

Distinct197
Distinct (%)10.8%
Missing162046
Missing (%)98.9%
Infinite0
Infinite (%)0.0%
Mean534.73707
Minimum120
Maximum1055
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:27.520385image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile301
Q1433
median533
Q3650
95-th percentile770
Maximum1055
Range935
Interquartile range (IQR)217

Descriptive statistics

Standard deviation141.43738
Coefficient of variation (CV)0.26449892
Kurtosis0.2824453
Mean534.73707
Median Absolute Deviation (MAD)108
Skewness-0.23219889
Sum972152
Variance20004.532
MonotonicityNot monotonic
2024-12-30T00:11:27.610466image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
656 134
 
0.1%
705 75
 
< 0.1%
560 59
 
< 0.1%
451 57
 
< 0.1%
650 55
 
< 0.1%
580 46
 
< 0.1%
800 45
 
< 0.1%
417 43
 
< 0.1%
423 39
 
< 0.1%
407 35
 
< 0.1%
Other values (187) 1230
 
0.8%
(Missing) 162046
98.9%
ValueCountFrequency (%)
120 2
 
< 0.1%
132 3
 
< 0.1%
135 1
 
< 0.1%
140 10
< 0.1%
145 6
< 0.1%
150 1
 
< 0.1%
155 3
 
< 0.1%
175 6
< 0.1%
190 5
 
< 0.1%
199 13
< 0.1%
ValueCountFrequency (%)
1055 1
 
< 0.1%
1010 1
 
< 0.1%
940 1
 
< 0.1%
930 1
 
< 0.1%
905 1
 
< 0.1%
850 1
 
< 0.1%
830 10
 
< 0.1%
822 14
 
< 0.1%
805 1
 
< 0.1%
800 45
< 0.1%
Distinct3427
Distinct (%)2.1%
Missing10
Missing (%)< 0.1%
Memory size1.3 MiB
2024-12-30T00:11:27.845680image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length50
Median length46
Mean length8.0162462
Min length1

Characters and Unicode

Total characters1313494
Distinct characters133
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique760 ?
Unique (%)0.5%

Sample

1st row1 опция
2nd row26 опций
3rd row1 опция
4th row19 опций
5th row16 опций
ValueCountFrequency (%)
опция 56105
19.0%
1 53851
18.2%
опций 25787
 
8.7%
опции 13791
 
4.7%
comfort 3993
 
1.4%
2 3731
 
1.3%
sport 2644
 
0.9%
3 2152
 
0.7%
luxury 2119
 
0.7%
luxe 2118
 
0.7%
Other values (1753) 129238
43.7%
2024-12-30T00:11:28.187991image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
131675
 
10.0%
и 112041
 
8.5%
о 100134
 
7.6%
ц 96008
 
7.3%
п 95997
 
7.3%
1 74190
 
5.6%
я 58569
 
4.5%
e 36737
 
2.8%
i 35254
 
2.7%
r 28393
 
2.2%
Other values (123) 544496
41.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1313494
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
131675
 
10.0%
и 112041
 
8.5%
о 100134
 
7.6%
ц 96008
 
7.3%
п 95997
 
7.3%
1 74190
 
5.6%
я 58569
 
4.5%
e 36737
 
2.8%
i 35254
 
2.7%
r 28393
 
2.2%
Other values (123) 544496
41.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1313494
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
131675
 
10.0%
и 112041
 
8.5%
о 100134
 
7.6%
ц 96008
 
7.3%
п 95997
 
7.3%
1 74190
 
5.6%
я 58569
 
4.5%
e 36737
 
2.8%
i 35254
 
2.7%
r 28393
 
2.2%
Other values (123) 544496
41.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1313494
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
131675
 
10.0%
и 112041
 
8.5%
о 100134
 
7.6%
ц 96008
 
7.3%
п 95997
 
7.3%
1 74190
 
5.6%
я 58569
 
4.5%
e 36737
 
2.8%
i 35254
 
2.7%
r 28393
 
2.2%
Other values (123) 544496
41.5%

transmission
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
механическая
73659 
автоматическая
62727 
роботизированная
14490 
вариатор
12988 

Length

Max length16
Median length14
Mean length12.802263
Min length8

Characters and Unicode

Total characters2097830
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowмеханическая
2nd rowмеханическая
3rd rowроботизированная
4th rowмеханическая
5th rowавтоматическая

Common Values

ValueCountFrequency (%)
механическая 73659
45.0%
автоматическая 62727
38.3%
роботизированная 14490
 
8.8%
вариатор 12988
 
7.9%

Length

2024-12-30T00:11:28.284079image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T00:11:28.365153image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
механическая 73659
45.0%
автоматическая 62727
38.3%
роботизированная 14490
 
8.8%
вариатор 12988
 
7.9%

Most occurring characters

ValueCountFrequency (%)
а 390455
18.6%
е 210045
10.0%
и 178354
8.5%
т 152932
 
7.3%
я 150876
 
7.2%
ч 136386
 
6.5%
с 136386
 
6.5%
м 136386
 
6.5%
к 136386
 
6.5%
о 119185
 
5.7%
Other values (6) 350439
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2097830
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
а 390455
18.6%
е 210045
10.0%
и 178354
8.5%
т 152932
 
7.3%
я 150876
 
7.2%
ч 136386
 
6.5%
с 136386
 
6.5%
м 136386
 
6.5%
к 136386
 
6.5%
о 119185
 
5.7%
Other values (6) 350439
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2097830
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
а 390455
18.6%
е 210045
10.0%
и 178354
8.5%
т 152932
 
7.3%
я 150876
 
7.2%
ч 136386
 
6.5%
с 136386
 
6.5%
м 136386
 
6.5%
к 136386
 
6.5%
о 119185
 
5.7%
Other values (6) 350439
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2097830
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
а 390455
18.6%
е 210045
10.0%
и 178354
8.5%
т 152932
 
7.3%
я 150876
 
7.2%
ч 136386
 
6.5%
с 136386
 
6.5%
м 136386
 
6.5%
к 136386
 
6.5%
о 119185
 
5.7%
Other values (6) 350439
16.7%

drive
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
передний
93008 
полный
60291 
задний
10565 

Length

Max length8
Median length8
Mean length7.1351853
Min length6

Characters and Unicode

Total characters1169200
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowпередний
2nd rowпередний
3rd rowпередний
4th rowпередний
5th rowпередний

Common Values

ValueCountFrequency (%)
передний 93008
56.8%
полный 60291
36.8%
задний 10565
 
6.4%

Length

2024-12-30T00:11:28.455234image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T00:11:28.533305image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
передний 93008
56.8%
полный 60291
36.8%
задний 10565
 
6.4%

Most occurring characters

ValueCountFrequency (%)
е 186016
15.9%
н 163864
14.0%
й 163864
14.0%
п 153299
13.1%
и 103573
8.9%
д 103573
8.9%
р 93008
8.0%
о 60291
 
5.2%
л 60291
 
5.2%
ы 60291
 
5.2%
Other values (2) 21130
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1169200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
е 186016
15.9%
н 163864
14.0%
й 163864
14.0%
п 153299
13.1%
и 103573
8.9%
д 103573
8.9%
р 93008
8.0%
о 60291
 
5.2%
л 60291
 
5.2%
ы 60291
 
5.2%
Other values (2) 21130
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1169200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
е 186016
15.9%
н 163864
14.0%
й 163864
14.0%
п 153299
13.1%
и 103573
8.9%
д 103573
8.9%
р 93008
8.0%
о 60291
 
5.2%
л 60291
 
5.2%
ы 60291
 
5.2%
Other values (2) 21130
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1169200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
е 186016
15.9%
н 163864
14.0%
й 163864
14.0%
п 153299
13.1%
и 103573
8.9%
д 103573
8.9%
р 93008
8.0%
о 60291
 
5.2%
л 60291
 
5.2%
ы 60291
 
5.2%
Other values (2) 21130
 
1.8%

st_wheel
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Левый
158830 
Правый
 
5034

Length

Max length6
Median length5
Mean length5.0307206
Min length5

Characters and Unicode

Total characters824354
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowЛевый
2nd rowЛевый
3rd rowЛевый
4th rowЛевый
5th rowЛевый

Common Values

ValueCountFrequency (%)
Левый 158830
96.9%
Правый 5034
 
3.1%

Length

2024-12-30T00:11:28.612377image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T00:11:28.682441image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
левый 158830
96.9%
правый 5034
 
3.1%

Most occurring characters

ValueCountFrequency (%)
ы 163864
19.9%
в 163864
19.9%
й 163864
19.9%
е 158830
19.3%
Л 158830
19.3%
П 5034
 
0.6%
р 5034
 
0.6%
а 5034
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 824354
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
ы 163864
19.9%
в 163864
19.9%
й 163864
19.9%
е 158830
19.3%
Л 158830
19.3%
П 5034
 
0.6%
р 5034
 
0.6%
а 5034
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 824354
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
ы 163864
19.9%
в 163864
19.9%
й 163864
19.9%
е 158830
19.3%
Л 158830
19.3%
П 5034
 
0.6%
р 5034
 
0.6%
а 5034
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 824354
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
ы 163864
19.9%
в 163864
19.9%
й 163864
19.9%
е 158830
19.3%
Л 158830
19.3%
П 5034
 
0.6%
р 5034
 
0.6%
а 5034
 
0.6%

condition
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Не требует ремонта
163864 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters2949552
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowНе требует ремонта
2nd rowНе требует ремонта
3rd rowНе требует ремонта
4th rowНе требует ремонта
5th rowНе требует ремонта

Common Values

ValueCountFrequency (%)
Не требует ремонта 163864
100.0%

Length

2024-12-30T00:11:28.755507image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T00:11:28.821567image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
не 163864
33.3%
требует 163864
33.3%
ремонта 163864
33.3%

Most occurring characters

ValueCountFrequency (%)
е 655456
22.2%
т 491592
16.7%
327728
11.1%
р 327728
11.1%
Н 163864
 
5.6%
б 163864
 
5.6%
у 163864
 
5.6%
м 163864
 
5.6%
о 163864
 
5.6%
н 163864
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2949552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
е 655456
22.2%
т 491592
16.7%
327728
11.1%
р 327728
11.1%
Н 163864
 
5.6%
б 163864
 
5.6%
у 163864
 
5.6%
м 163864
 
5.6%
о 163864
 
5.6%
н 163864
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2949552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
е 655456
22.2%
т 491592
16.7%
327728
11.1%
р 327728
11.1%
Н 163864
 
5.6%
б 163864
 
5.6%
у 163864
 
5.6%
м 163864
 
5.6%
о 163864
 
5.6%
н 163864
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2949552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
е 655456
22.2%
т 491592
16.7%
327728
11.1%
р 327728
11.1%
Н 163864
 
5.6%
б 163864
 
5.6%
у 163864
 
5.6%
м 163864
 
5.6%
о 163864
 
5.6%
н 163864
 
5.6%

count_owner
Categorical

Distinct3
Distinct (%)< 0.1%
Missing47
Missing (%)< 0.1%
Memory size1.3 MiB
3.0
73831 
1.0
54087 
2.0
35899 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters491451
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 73831
45.1%
1.0 54087
33.0%
2.0 35899
21.9%
(Missing) 47
 
< 0.1%

Length

2024-12-30T00:11:28.891631image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T00:11:28.962695image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
3.0 73831
45.1%
1.0 54087
33.0%
2.0 35899
21.9%

Most occurring characters

ValueCountFrequency (%)
. 163817
33.3%
0 163817
33.3%
3 73831
15.0%
1 54087
 
11.0%
2 35899
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 491451
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 163817
33.3%
0 163817
33.3%
3 73831
15.0%
1 54087
 
11.0%
2 35899
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 491451
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 163817
33.3%
0 163817
33.3%
3 73831
15.0%
1 54087
 
11.0%
2 35899
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 491451
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 163817
33.3%
0 163817
33.3%
3 73831
15.0%
1 54087
 
11.0%
2 35899
 
7.3%

original_pts
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing842
Missing (%)0.5%
Memory size1.3 MiB
Оригинал
133240 
Дубликат
29782 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1304176
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowОригинал
2nd rowОригинал
3rd rowОригинал
4th rowОригинал
5th rowОригинал

Common Values

ValueCountFrequency (%)
Оригинал 133240
81.3%
Дубликат 29782
 
18.2%
(Missing) 842
 
0.5%

Length

2024-12-30T00:11:29.040766image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T00:11:29.108828image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
оригинал 133240
81.7%
дубликат 29782
 
18.3%

Most occurring characters

ValueCountFrequency (%)
и 296262
22.7%
а 163022
12.5%
л 163022
12.5%
О 133240
10.2%
г 133240
10.2%
р 133240
10.2%
н 133240
10.2%
Д 29782
 
2.3%
у 29782
 
2.3%
б 29782
 
2.3%
Other values (2) 59564
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1304176
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
и 296262
22.7%
а 163022
12.5%
л 163022
12.5%
О 133240
10.2%
г 133240
10.2%
р 133240
10.2%
н 133240
10.2%
Д 29782
 
2.3%
у 29782
 
2.3%
б 29782
 
2.3%
Other values (2) 59564
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1304176
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
и 296262
22.7%
а 163022
12.5%
л 163022
12.5%
О 133240
10.2%
г 133240
10.2%
р 133240
10.2%
н 133240
10.2%
Д 29782
 
2.3%
у 29782
 
2.3%
б 29782
 
2.3%
Other values (2) 59564
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1304176
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
и 296262
22.7%
а 163022
12.5%
л 163022
12.5%
О 133240
10.2%
г 133240
10.2%
р 133240
10.2%
н 133240
10.2%
Д 29782
 
2.3%
у 29782
 
2.3%
б 29782
 
2.3%
Other values (2) 59564
 
4.6%

customs
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Растаможен
163848 
Растаможен, нет\xa0ПТС
 
15
Растаможен, нет ПТС
 
1

Length

Max length22
Median length10
Mean length10.001153
Min length10

Characters and Unicode

Total characters1638829
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowРастаможен
2nd rowРастаможен
3rd rowРастаможен
4th rowРастаможен
5th rowРастаможен

Common Values

ValueCountFrequency (%)
Растаможен 163848
> 99.9%
Растаможен, нет\xa0ПТС 15
 
< 0.1%
Растаможен, нет ПТС 1
 
< 0.1%

Length

2024-12-30T00:11:29.187900image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T00:11:29.262969image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
растаможен 163864
> 99.9%
нет\xa0птс 15
 
< 0.1%
нет 1
 
< 0.1%
птс 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
а 327728
20.0%
е 163880
10.0%
т 163880
10.0%
н 163880
10.0%
Р 163864
10.0%
с 163864
10.0%
м 163864
10.0%
ж 163864
10.0%
о 163864
10.0%
, 16
 
< 0.1%
Other values (9) 125
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1638829
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
а 327728
20.0%
е 163880
10.0%
т 163880
10.0%
н 163880
10.0%
Р 163864
10.0%
с 163864
10.0%
м 163864
10.0%
ж 163864
10.0%
о 163864
10.0%
, 16
 
< 0.1%
Other values (9) 125
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1638829
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
а 327728
20.0%
е 163880
10.0%
т 163880
10.0%
н 163880
10.0%
Р 163864
10.0%
с 163864
10.0%
м 163864
10.0%
ж 163864
10.0%
о 163864
10.0%
, 16
 
< 0.1%
Other values (9) 125
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1638829
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
а 327728
20.0%
е 163880
10.0%
т 163880
10.0%
н 163880
10.0%
Р 163864
10.0%
с 163864
10.0%
м 163864
10.0%
ж 163864
10.0%
о 163864
10.0%
, 16
 
< 0.1%
Other values (9) 125
 
< 0.1%

state_mark
Categorical

High correlation 

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Россия
37893 
Германия
23943 
Япония
20192 
США
18841 
Китай
15158 
Other values (35)
47837 

Length

Max length29
Median length15
Mean length6.5492482
Min length3

Characters and Unicode

Total characters1073186
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowИталия
2nd rowИталия
3rd rowИталия
4th rowИталия
5th rowИталия

Common Values

ValueCountFrequency (%)
Россия 37893
23.1%
Германия 23943
14.6%
Япония 20192
12.3%
США 18841
11.5%
Китай 15158
9.3%
Южная Корея 8884
 
5.4%
Франция 8857
 
5.4%
Япония 7233
 
4.4%
Великобритания 4380
 
2.7%
Швеция 3492
 
2.1%
Other values (30) 14991
 
9.1%

Length

2024-12-30T00:11:29.344042image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
россия 38082
22.0%
япония 27425
15.9%
германия 26204
15.2%
сша 19001
11.0%
китай 16708
9.7%
франция 11235
 
6.5%
южная 9092
 
5.3%
корея 9092
 
5.3%
великобритания 5082
 
2.9%
чехия 4859
 
2.8%
Other values (17) 6178
 
3.6%

Most occurring characters

ValueCountFrequency (%)
и 145857
13.6%
я 136167
12.7%
н 80507
 
7.5%
о 79684
 
7.4%
с 76851
 
7.2%
а 71682
 
6.7%
р 52419
 
4.9%
е 49066
 
4.6%
Р 38144
 
3.6%
п 27801
 
2.6%
Other values (38) 315008
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1073186
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
и 145857
13.6%
я 136167
12.7%
н 80507
 
7.5%
о 79684
 
7.4%
с 76851
 
7.2%
а 71682
 
6.7%
р 52419
 
4.9%
е 49066
 
4.6%
Р 38144
 
3.6%
п 27801
 
2.6%
Other values (38) 315008
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1073186
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
и 145857
13.6%
я 136167
12.7%
н 80507
 
7.5%
о 79684
 
7.4%
с 76851
 
7.2%
а 71682
 
6.7%
р 52419
 
4.9%
е 49066
 
4.6%
Р 38144
 
3.6%
п 27801
 
2.6%
Other values (38) 315008
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1073186
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
и 145857
13.6%
я 136167
12.7%
н 80507
 
7.5%
о 79684
 
7.4%
с 76851
 
7.2%
а 71682
 
6.7%
р 52419
 
4.9%
е 49066
 
4.6%
Р 38144
 
3.6%
п 27801
 
2.6%
Other values (38) 315008
29.4%

class_auto
Categorical

High correlation 

Distinct9
Distinct (%)< 0.1%
Missing439
Missing (%)0.3%
Memory size1.3 MiB
B
43752 
C
41806 
D
25979 
J
20911 
E
17387 
Other values (4)
13590 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters163425
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
B 43752
26.7%
C 41806
25.5%
D 25979
15.9%
J 20911
12.8%
E 17387
 
10.6%
M 5350
 
3.3%
F 3486
 
2.1%
A 3264
 
2.0%
S 1490
 
0.9%
(Missing) 439
 
0.3%

Length

2024-12-30T00:11:29.422113image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T00:11:29.506189image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
b 43752
26.8%
c 41806
25.6%
d 25979
15.9%
j 20911
12.8%
e 17387
 
10.6%
m 5350
 
3.3%
f 3486
 
2.1%
a 3264
 
2.0%
s 1490
 
0.9%

Most occurring characters

ValueCountFrequency (%)
B 43752
26.8%
C 41806
25.6%
D 25979
15.9%
J 20911
12.8%
E 17387
 
10.6%
M 5350
 
3.3%
F 3486
 
2.1%
A 3264
 
2.0%
S 1490
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 163425
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 43752
26.8%
C 41806
25.6%
D 25979
15.9%
J 20911
12.8%
E 17387
 
10.6%
M 5350
 
3.3%
F 3486
 
2.1%
A 3264
 
2.0%
S 1490
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 163425
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 43752
26.8%
C 41806
25.6%
D 25979
15.9%
J 20911
12.8%
E 17387
 
10.6%
M 5350
 
3.3%
F 3486
 
2.1%
A 3264
 
2.0%
S 1490
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 163425
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 43752
26.8%
C 41806
25.6%
D 25979
15.9%
J 20911
12.8%
E 17387
 
10.6%
M 5350
 
3.3%
F 3486
 
2.1%
A 3264
 
2.0%
S 1490
 
0.9%

door_count
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5500232
Minimum0
Maximum5
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:29.591267image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median5
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.66715147
Coefficient of variation (CV)0.14662595
Kurtosis2.4729551
Mean4.5500232
Median Absolute Deviation (MAD)0
Skewness-1.5523111
Sum745585
Variance0.44509108
MonotonicityNot monotonic
2024-12-30T00:11:29.660329image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 103319
63.1%
4 50347
30.7%
3 7211
 
4.4%
2 2984
 
1.8%
0 2
 
< 0.1%
1 1
 
< 0.1%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 1
 
< 0.1%
2 2984
 
1.8%
3 7211
 
4.4%
4 50347
30.7%
5 103319
63.1%
ValueCountFrequency (%)
5 103319
63.1%
4 50347
30.7%
3 7211
 
4.4%
2 2984
 
1.8%
1 1
 
< 0.1%
0 2
 
< 0.1%

long
Real number (ℝ)

High correlation 

Distinct1375
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4478.9061
Minimum2488
Maximum6330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:29.741403image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum2488
5-th percentile3765
Q14260
median4483
Q34735
95-th percentile5039
Maximum6330
Range3842
Interquartile range (IQR)475

Descriptive statistics

Standard deviation380.05614
Coefficient of variation (CV)0.08485468
Kurtosis1.3014989
Mean4478.9061
Median Absolute Deviation (MAD)234
Skewness-0.20855548
Sum7.3393147 × 108
Variance144442.67
MonotonicityNot monotonic
2024-12-30T00:11:29.841494image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4122 4619
 
2.8%
4330 3518
 
2.1%
4265 2559
 
1.6%
4048 2548
 
1.6%
3720 2371
 
1.4%
4410 2183
 
1.3%
4170 2162
 
1.3%
4500 2009
 
1.2%
4268 1750
 
1.1%
4237 1582
 
1.0%
Other values (1365) 138563
84.6%
ValueCountFrequency (%)
2488 1
 
< 0.1%
2499 20
 
< 0.1%
2500 21
 
< 0.1%
2595 3
 
< 0.1%
2625 1
 
< 0.1%
2695 122
0.1%
2752 2
 
< 0.1%
2885 1
 
< 0.1%
2894 1
 
< 0.1%
2900 2
 
< 0.1%
ValueCountFrequency (%)
6330 5
< 0.1%
6305 1
 
< 0.1%
6297 6
< 0.1%
6294 1
 
< 0.1%
6238 9
< 0.1%
6200 1
 
< 0.1%
6185 4
 
< 0.1%
6167 1
 
< 0.1%
6165 9
< 0.1%
6133 10
< 0.1%

width
Real number (ℝ)

High correlation 

Distinct497
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1791.7752
Minimum1000
Maximum2360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:29.942586image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1636
Q11700
median1795
Q31860
95-th percentile1984
Maximum2360
Range1360
Interquartile range (IQR)160

Descriptive statistics

Standard deviation113.56453
Coefficient of variation (CV)0.063381012
Kurtosis0.67442263
Mean1791.7752
Median Absolute Deviation (MAD)83
Skewness0.19860795
Sum2.9360746 × 108
Variance12896.902
MonotonicityNot monotonic
2024-12-30T00:11:30.043677image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1680 10981
 
6.7%
1650 8008
 
4.9%
1700 7121
 
4.3%
1800 3897
 
2.4%
1770 3528
 
2.2%
1810 2818
 
1.7%
1840 2700
 
1.6%
1620 2613
 
1.6%
1815 2313
 
1.4%
1725 2175
 
1.3%
Other values (487) 117710
71.8%
ValueCountFrequency (%)
1000 1
 
< 0.1%
1312 2
 
< 0.1%
1316 1
 
< 0.1%
1372 1
 
< 0.1%
1380 3
 
< 0.1%
1382 2
 
< 0.1%
1395 40
< 0.1%
1400 24
< 0.1%
1410 1
 
< 0.1%
1415 5
 
< 0.1%
ValueCountFrequency (%)
2360 51
 
< 0.1%
2300 2
 
< 0.1%
2235 97
0.1%
2220 216
0.1%
2201 13
 
< 0.1%
2199 6
 
< 0.1%
2197 10
 
< 0.1%
2190 1
 
< 0.1%
2183 10
 
< 0.1%
2177 3
 
< 0.1%

height
Real number (ℝ)

High correlation 

Distinct727
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1576.2366
Minimum1000
Maximum2450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:30.138764image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1402
Q11454
median1517
Q31685
95-th percentile1875
Maximum2450
Range1450
Interquartile range (IQR)231

Descriptive statistics

Standard deviation151.09854
Coefficient of variation (CV)0.095860315
Kurtosis-0.0080427454
Mean1576.2366
Median Absolute Deviation (MAD)103
Skewness0.72179207
Sum2.5828844 × 108
Variance22830.769
MonotonicityNot monotonic
2024-12-30T00:11:30.236853image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1402 7834
 
4.8%
1420 6696
 
4.1%
1500 6559
 
4.0%
1640 3901
 
2.4%
1705 3207
 
2.0%
1445 2792
 
1.7%
1652 2606
 
1.6%
1695 2528
 
1.5%
1440 2172
 
1.3%
1470 2165
 
1.3%
Other values (717) 123404
75.3%
ValueCountFrequency (%)
1000 1
 
< 0.1%
1045 1
 
< 0.1%
1070 2
 
< 0.1%
1075 1
 
< 0.1%
1105 1
 
< 0.1%
1120 4
< 0.1%
1129 2
 
< 0.1%
1130 1
 
< 0.1%
1135 3
 
< 0.1%
1136 8
< 0.1%
ValueCountFrequency (%)
2450 4
 
< 0.1%
2320 1
 
< 0.1%
2300 2
 
< 0.1%
2285 1
 
< 0.1%
2280 1
 
< 0.1%
2235 5
 
< 0.1%
2176 20
< 0.1%
2148 3
 
< 0.1%
2126 11
< 0.1%
2105 3
 
< 0.1%

v_tank
Real number (ℝ)

High correlation  Missing 

Distinct103
Distinct (%)0.1%
Missing53386
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean61.220686
Minimum16
Maximum166
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:30.331940image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile43
Q151
median60
Q370
95-th percentile87
Maximum166
Range150
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.033058
Coefficient of variation (CV)0.22922085
Kurtosis1.2711302
Mean61.220686
Median Absolute Deviation (MAD)9
Skewness0.84264182
Sum6763539
Variance196.92671
MonotonicityNot monotonic
2024-12-30T00:11:30.428027image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 14744
 
9.0%
50 13690
 
8.4%
55 10991
 
6.7%
70 5797
 
3.5%
65 4748
 
2.9%
45 3614
 
2.2%
75 3344
 
2.0%
58 2868
 
1.8%
80 2748
 
1.7%
51 2636
 
1.6%
Other values (93) 45298
27.6%
(Missing) 53386
32.6%
ValueCountFrequency (%)
16 1
 
< 0.1%
20 1
 
< 0.1%
22 20
 
< 0.1%
24 2
 
< 0.1%
26 1
 
< 0.1%
27 187
0.1%
28 90
 
0.1%
30 382
0.2%
31 11
 
< 0.1%
32 184
0.1%
ValueCountFrequency (%)
166 2
 
< 0.1%
159 3
 
< 0.1%
140 17
 
< 0.1%
136 8
 
< 0.1%
132 2
 
< 0.1%
130 2
 
< 0.1%
129 8
 
< 0.1%
127 6
 
< 0.1%
125 97
0.1%
123 1
 
< 0.1%

curb_weight
Real number (ℝ)

High correlation  Missing 

Distinct1422
Distinct (%)0.9%
Missing3672
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean1470.0675
Minimum399
Maximum4103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:30.525114image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum399
5-th percentile970
Q11151
median1395
Q31707
95-th percentile2278
Maximum4103
Range3704
Interquartile range (IQR)556

Descriptive statistics

Standard deviation416.84809
Coefficient of variation (CV)0.28355711
Kurtosis0.32009954
Mean1470.0675
Median Absolute Deviation (MAD)270
Skewness0.85477755
Sum2.3549306 × 108
Variance173762.33
MonotonicityNot monotonic
2024-12-30T00:11:30.624205image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
970 3306
 
2.0%
985 2899
 
1.8%
1075 2883
 
1.8%
1210 2593
 
1.6%
1400 2415
 
1.5%
1410 2122
 
1.3%
1080 1895
 
1.2%
1040 1837
 
1.1%
1540 1697
 
1.0%
1085 1415
 
0.9%
Other values (1412) 137130
83.7%
(Missing) 3672
 
2.2%
ValueCountFrequency (%)
399 2
 
< 0.1%
425 1
 
< 0.1%
454 3
 
< 0.1%
475 1
 
< 0.1%
550 3
 
< 0.1%
600 9
< 0.1%
615 2
 
< 0.1%
620 18
< 0.1%
635 11
< 0.1%
640 1
 
< 0.1%
ValueCountFrequency (%)
4103 9
< 0.1%
3950 1
 
< 0.1%
3850 1
 
< 0.1%
3370 1
 
< 0.1%
3335 4
 
< 0.1%
3305 1
 
< 0.1%
3300 1
 
< 0.1%
3270 2
 
< 0.1%
3245 2
 
< 0.1%
3242 11
< 0.1%

gross_weight
Real number (ℝ)

High correlation  Missing 

Distinct988
Distinct (%)0.7%
Missing15621
Missing (%)9.5%
Infinite0
Infinite (%)0.0%
Mean1970.7765
Minimum810
Maximum5000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:30.724296image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum810
5-th percentile1395
Q11593
median1850
Q32220
95-th percentile2990
Maximum5000
Range4190
Interquartile range (IQR)627

Descriptive statistics

Standard deviation494.27992
Coefficient of variation (CV)0.25080466
Kurtosis0.31142828
Mean1970.7765
Median Absolute Deviation (MAD)290
Skewness0.93687455
Sum2.9215382 × 108
Variance244312.63
MonotonicityNot monotonic
2024-12-30T00:11:30.817380image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1560 5205
 
3.2%
1410 4274
 
2.6%
1610 3514
 
2.1%
1515 2420
 
1.5%
1860 2412
 
1.5%
1555 2162
 
1.3%
1850 2087
 
1.3%
1650 1967
 
1.2%
1395 1826
 
1.1%
1578 1677
 
1.0%
Other values (978) 120699
73.7%
(Missing) 15621
 
9.5%
ValueCountFrequency (%)
810 1
 
< 0.1%
895 4
 
< 0.1%
920 7
 
< 0.1%
940 1
 
< 0.1%
950 3
 
< 0.1%
960 6
 
< 0.1%
975 109
0.1%
980 25
 
< 0.1%
985 67
< 0.1%
990 111
0.1%
ValueCountFrequency (%)
5000 3
 
< 0.1%
4581 3
 
< 0.1%
4500 1
 
< 0.1%
4400 2
 
< 0.1%
4250 1
 
< 0.1%
4173 2
 
< 0.1%
4080 1
 
< 0.1%
4037 1
 
< 0.1%
3992 28
< 0.1%
3946 1
 
< 0.1%

front_brakes
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
дисковые
162030 
барабанные
 
1524
керамические
 
310

Length

Max length12
Median length8
Mean length8.026168
Min length8

Characters and Unicode

Total characters1315200
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowдисковые
2nd rowдисковые
3rd rowдисковые
4th rowдисковые
5th rowдисковые

Common Values

ValueCountFrequency (%)
дисковые 162030
98.9%
барабанные 1524
 
0.9%
керамические 310
 
0.2%

Length

2024-12-30T00:11:30.911466image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T00:11:30.990538image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
дисковые 162030
98.9%
барабанные 1524
 
0.9%
керамические 310
 
0.2%

Most occurring characters

ValueCountFrequency (%)
е 164484
12.5%
ы 163554
12.4%
и 162650
12.4%
к 162650
12.4%
с 162340
12.3%
д 162030
12.3%
в 162030
12.3%
о 162030
12.3%
а 4882
 
0.4%
б 3048
 
0.2%
Other values (4) 5502
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1315200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
е 164484
12.5%
ы 163554
12.4%
и 162650
12.4%
к 162650
12.4%
с 162340
12.3%
д 162030
12.3%
в 162030
12.3%
о 162030
12.3%
а 4882
 
0.4%
б 3048
 
0.2%
Other values (4) 5502
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1315200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
е 164484
12.5%
ы 163554
12.4%
и 162650
12.4%
к 162650
12.4%
с 162340
12.3%
д 162030
12.3%
в 162030
12.3%
о 162030
12.3%
а 4882
 
0.4%
б 3048
 
0.2%
Other values (4) 5502
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1315200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
е 164484
12.5%
ы 163554
12.4%
и 162650
12.4%
к 162650
12.4%
с 162340
12.3%
д 162030
12.3%
в 162030
12.3%
о 162030
12.3%
а 4882
 
0.4%
б 3048
 
0.2%
Other values (4) 5502
 
0.4%

rear_brakes
Categorical

High correlation  Missing 

Distinct3
Distinct (%)< 0.1%
Missing1884
Missing (%)1.1%
Memory size1.3 MiB
дисковые
99266 
барабанные
62521 
керамические
 
193

Length

Max length12
Median length8
Mean length8.7767255
Min length8

Characters and Unicode

Total characters1421654
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowдисковые
2nd rowдисковые
3rd rowдисковые
4th rowдисковые
5th rowдисковые

Common Values

ValueCountFrequency (%)
дисковые 99266
60.6%
барабанные 62521
38.2%
керамические 193
 
0.1%
(Missing) 1884
 
1.1%

Length

2024-12-30T00:11:31.075615image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T00:11:31.154687image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
дисковые 99266
61.3%
барабанные 62521
38.6%
керамические 193
 
0.1%

Most occurring characters

ValueCountFrequency (%)
а 187756
13.2%
е 162366
11.4%
ы 161787
11.4%
б 125042
8.8%
н 125042
8.8%
и 99652
7.0%
к 99652
7.0%
с 99459
7.0%
д 99266
7.0%
о 99266
7.0%
Other values (4) 162366
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1421654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
а 187756
13.2%
е 162366
11.4%
ы 161787
11.4%
б 125042
8.8%
н 125042
8.8%
и 99652
7.0%
к 99652
7.0%
с 99459
7.0%
д 99266
7.0%
о 99266
7.0%
Other values (4) 162366
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1421654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
а 187756
13.2%
е 162366
11.4%
ы 161787
11.4%
б 125042
8.8%
н 125042
8.8%
и 99652
7.0%
к 99652
7.0%
с 99459
7.0%
д 99266
7.0%
о 99266
7.0%
Other values (4) 162366
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1421654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
а 187756
13.2%
е 162366
11.4%
ы 161787
11.4%
б 125042
8.8%
н 125042
8.8%
и 99652
7.0%
к 99652
7.0%
с 99459
7.0%
д 99266
7.0%
о 99266
7.0%
Other values (4) 162366
11.4%

max_speed
Real number (ℝ)

High correlation  Missing 

Distinct232
Distinct (%)0.1%
Missing6198
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean186.93755
Minimum60
Maximum355
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:31.234760image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile140
Q1170
median183
Q3202
95-th percentile250
Maximum355
Range295
Interquartile range (IQR)32

Descriptive statistics

Standard deviation31.195413
Coefficient of variation (CV)0.16687612
Kurtosis1.7297994
Mean186.93755
Median Absolute Deviation (MAD)17
Skewness0.74301996
Sum29473695
Variance973.1538
MonotonicityNot monotonic
2024-12-30T00:11:31.329846image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180 9471
 
5.8%
175 7052
 
4.3%
185 6641
 
4.1%
160 6151
 
3.8%
170 5945
 
3.6%
210 5868
 
3.6%
200 5781
 
3.5%
190 5694
 
3.5%
165 5510
 
3.4%
250 5122
 
3.1%
Other values (222) 94431
57.6%
(Missing) 6198
 
3.8%
ValueCountFrequency (%)
60 1
 
< 0.1%
70 5
 
< 0.1%
75 1
 
< 0.1%
85 72
 
< 0.1%
87 1
 
< 0.1%
88 33
 
< 0.1%
90 227
0.1%
95 3
 
< 0.1%
100 131
0.1%
101 15
 
< 0.1%
ValueCountFrequency (%)
355 1
 
< 0.1%
350 20
< 0.1%
343 1
 
< 0.1%
342 1
 
< 0.1%
341 8
 
< 0.1%
340 39
< 0.1%
335 12
 
< 0.1%
334 5
 
< 0.1%
333 26
< 0.1%
332 1
 
< 0.1%

acceleration
Real number (ℝ)

High correlation  Missing 

Distinct239
Distinct (%)0.2%
Missing58350
Missing (%)35.6%
Infinite0
Infinite (%)0.0%
Mean10.743199
Minimum2.02
Maximum39.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:31.422931image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum2.02
5-th percentile5.2
Q18.4
median10.7
Q312.4
95-th percentile17
Maximum39.2
Range37.18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.8669574
Coefficient of variation (CV)0.3599447
Kurtosis6.9214902
Mean10.743199
Median Absolute Deviation (MAD)2
Skewness1.6042258
Sum1133557.9
Variance14.95336
MonotonicityNot monotonic
2024-12-30T00:11:31.519018image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.5 3278
 
2.0%
11 2762
 
1.7%
10.5 2372
 
1.4%
11.5 2297
 
1.4%
11.7 2006
 
1.2%
9.8 1911
 
1.2%
11.2 1887
 
1.2%
9.9 1787
 
1.1%
17 1670
 
1.0%
10.7 1629
 
1.0%
Other values (229) 83915
51.2%
(Missing) 58350
35.6%
ValueCountFrequency (%)
2.02 11
 
< 0.1%
2.1 10
 
< 0.1%
2.3 7
 
< 0.1%
2.5 20
 
< 0.1%
2.6 8
 
< 0.1%
2.7 30
 
< 0.1%
2.78 2
 
< 0.1%
2.8 107
0.1%
2.84 10
 
< 0.1%
2.85 1
 
< 0.1%
ValueCountFrequency (%)
39.2 17
 
< 0.1%
37 18
 
< 0.1%
35 276
0.2%
34.7 21
 
< 0.1%
34.2 20
 
< 0.1%
34 22
 
< 0.1%
33 5
 
< 0.1%
32 54
 
< 0.1%
30 11
 
< 0.1%
29.5 1
 
< 0.1%

fuel_cons
Real number (ℝ)

High correlation  Missing 

Distinct144
Distinct (%)0.1%
Missing65200
Missing (%)39.8%
Infinite0
Infinite (%)0.0%
Mean6.7536835
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:31.613104image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.7
Q15.5
median6.3
Q37.5
95-th percentile10.6
Maximum31
Range30
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8668784
Coefficient of variation (CV)0.27642374
Kurtosis4.7179148
Mean6.7536835
Median Absolute Deviation (MAD)0.9
Skewness1.726092
Sum666345.43
Variance3.485235
MonotonicityNot monotonic
2024-12-30T00:11:31.703186image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.3 4756
 
2.9%
5.8 4677
 
2.9%
5.7 3424
 
2.1%
5.4 3266
 
2.0%
5.5 3239
 
2.0%
6.2 3219
 
2.0%
6.4 3196
 
2.0%
5.6 2864
 
1.7%
5.3 2798
 
1.7%
5.2 2786
 
1.7%
Other values (134) 64439
39.3%
(Missing) 65200
39.8%
ValueCountFrequency (%)
1 2
 
< 0.1%
1.4 8
 
< 0.1%
1.5 1
 
< 0.1%
1.6 2
 
< 0.1%
2.7 2
 
< 0.1%
2.9 10
 
< 0.1%
3 1
 
< 0.1%
3.1 9
 
< 0.1%
3.2 24
< 0.1%
3.3 32
< 0.1%
ValueCountFrequency (%)
31 1
 
< 0.1%
25.2 3
 
< 0.1%
20 6
 
< 0.1%
18.3 1
 
< 0.1%
18.1 1
 
< 0.1%
17.2 12
 
< 0.1%
17 1
 
< 0.1%
16.8 93
0.1%
16.2 161
0.1%
16.1 2
 
< 0.1%

fuel_brand
Categorical

High correlation  Missing 

Distinct7
Distinct (%)< 0.1%
Missing2420
Missing (%)1.5%
Memory size1.3 MiB
АИ-95
104777 
АИ-92
32561 
ДТ
17130 
АИ-98
 
4978
АИ-80
 
1004
Other values (2)
 
994

Length

Max length12
Median length5
Mean length4.6849806
Min length2

Characters and Unicode

Total characters756362
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowАИ-95
2nd rowАИ-95
3rd rowАИ-95
4th rowАИ-98
5th rowАИ-95

Common Values

ValueCountFrequency (%)
АИ-95 104777
63.9%
АИ-92 32561
 
19.9%
ДТ 17130
 
10.5%
АИ-98 4978
 
3.0%
АИ-80 1004
 
0.6%
АИ-76 918
 
0.6%
Газ (Бензин) 76
 
< 0.1%
(Missing) 2420
 
1.5%

Length

2024-12-30T00:11:31.792266image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T00:11:31.871338image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
аи-95 104777
64.9%
аи-92 32561
 
20.2%
дт 17130
 
10.6%
аи-98 4978
 
3.1%
аи-80 1004
 
0.6%
аи-76 918
 
0.6%
газ 76
 
< 0.1%
бензин 76
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
А 144238
19.1%
И 144238
19.1%
- 144238
19.1%
9 142316
18.8%
5 104777
13.9%
2 32561
 
4.3%
Д 17130
 
2.3%
Т 17130
 
2.3%
8 5982
 
0.8%
0 1004
 
0.1%
Other values (12) 2748
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 756362
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
А 144238
19.1%
И 144238
19.1%
- 144238
19.1%
9 142316
18.8%
5 104777
13.9%
2 32561
 
4.3%
Д 17130
 
2.3%
Т 17130
 
2.3%
8 5982
 
0.8%
0 1004
 
0.1%
Other values (12) 2748
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 756362
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
А 144238
19.1%
И 144238
19.1%
- 144238
19.1%
9 142316
18.8%
5 104777
13.9%
2 32561
 
4.3%
Д 17130
 
2.3%
Т 17130
 
2.3%
8 5982
 
0.8%
0 1004
 
0.1%
Other values (12) 2748
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 756362
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
А 144238
19.1%
И 144238
19.1%
- 144238
19.1%
9 142316
18.8%
5 104777
13.9%
2 32561
 
4.3%
Д 17130
 
2.3%
Т 17130
 
2.3%
8 5982
 
0.8%
0 1004
 
0.1%
Other values (12) 2748
 
0.4%

engine_loc1
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)< 0.1%
Missing1939
Missing (%)1.2%
Memory size1.3 MiB
переднее
160925 
заднее
 
617
центральное
 
383

Length

Max length11
Median length8
Mean length7.9994751
Min length6

Characters and Unicode

Total characters1295315
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowпереднее
2nd rowпереднее
3rd rowпереднее
4th rowпереднее
5th rowпереднее

Common Values

ValueCountFrequency (%)
переднее 160925
98.2%
заднее 617
 
0.4%
центральное 383
 
0.2%
(Missing) 1939
 
1.2%

Length

2024-12-30T00:11:31.962421image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T00:11:32.033486image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
переднее 160925
99.4%
заднее 617
 
0.4%
центральное 383
 
0.2%

Most occurring characters

ValueCountFrequency (%)
е 645700
49.8%
н 162308
 
12.5%
д 161542
 
12.5%
р 161308
 
12.5%
п 160925
 
12.4%
а 1000
 
0.1%
з 617
 
< 0.1%
ц 383
 
< 0.1%
т 383
 
< 0.1%
л 383
 
< 0.1%
Other values (2) 766
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1295315
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
е 645700
49.8%
н 162308
 
12.5%
д 161542
 
12.5%
р 161308
 
12.5%
п 160925
 
12.4%
а 1000
 
0.1%
з 617
 
< 0.1%
ц 383
 
< 0.1%
т 383
 
< 0.1%
л 383
 
< 0.1%
Other values (2) 766
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1295315
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
е 645700
49.8%
н 162308
 
12.5%
д 161542
 
12.5%
р 161308
 
12.5%
п 160925
 
12.4%
а 1000
 
0.1%
з 617
 
< 0.1%
ц 383
 
< 0.1%
т 383
 
< 0.1%
л 383
 
< 0.1%
Other values (2) 766
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1295315
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
е 645700
49.8%
н 162308
 
12.5%
д 161542
 
12.5%
р 161308
 
12.5%
п 160925
 
12.4%
а 1000
 
0.1%
з 617
 
< 0.1%
ц 383
 
< 0.1%
т 383
 
< 0.1%
л 383
 
< 0.1%
Other values (2) 766
 
0.1%

engine_loc2
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing2939
Missing (%)1.8%
Memory size1.3 MiB
поперечное
111604 
продольное
49321 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1609250
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowпоперечное
2nd rowпоперечное
3rd rowпоперечное
4th rowпоперечное
5th rowпоперечное

Common Values

ValueCountFrequency (%)
поперечное 111604
68.1%
продольное 49321
30.1%
(Missing) 2939
 
1.8%

Length

2024-12-30T00:11:32.108554image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T00:11:32.176616image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
поперечное 111604
69.4%
продольное 49321
30.6%

Most occurring characters

ValueCountFrequency (%)
е 384133
23.9%
о 371171
23.1%
п 272529
16.9%
р 160925
10.0%
н 160925
10.0%
ч 111604
 
6.9%
д 49321
 
3.1%
л 49321
 
3.1%
ь 49321
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1609250
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
е 384133
23.9%
о 371171
23.1%
п 272529
16.9%
р 160925
10.0%
н 160925
10.0%
ч 111604
 
6.9%
д 49321
 
3.1%
л 49321
 
3.1%
ь 49321
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1609250
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
е 384133
23.9%
о 371171
23.1%
п 272529
16.9%
р 160925
10.0%
н 160925
10.0%
ч 111604
 
6.9%
д 49321
 
3.1%
л 49321
 
3.1%
ь 49321
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1609250
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
е 384133
23.9%
о 371171
23.1%
п 272529
16.9%
р 160925
10.0%
н 160925
10.0%
ч 111604
 
6.9%
д 49321
 
3.1%
л 49321
 
3.1%
ь 49321
 
3.1%

turbocharg
Categorical

High correlation  Missing 

Distinct3
Distinct (%)< 0.1%
Missing1891
Missing (%)1.2%
Memory size1.3 MiB
нет
109425 
турбонаддув
51652 
компрессор
 
896

Length

Max length11
Median length3
Mean length5.5898637
Min length3

Characters and Unicode

Total characters905407
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowтурбонаддув
2nd rowтурбонаддув
3rd rowтурбонаддув
4th rowтурбонаддув
5th rowтурбонаддув

Common Values

ValueCountFrequency (%)
нет 109425
66.8%
турбонаддув 51652
31.5%
компрессор 896
 
0.5%
(Missing) 1891
 
1.2%

Length

2024-12-30T00:11:32.257689image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T00:11:32.334760image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
нет 109425
67.6%
турбонаддув 51652
31.9%
компрессор 896
 
0.6%

Most occurring characters

ValueCountFrequency (%)
н 161077
17.8%
т 161077
17.8%
е 110321
12.2%
у 103304
11.4%
д 103304
11.4%
р 53444
 
5.9%
о 53444
 
5.9%
б 51652
 
5.7%
а 51652
 
5.7%
в 51652
 
5.7%
Other values (4) 4480
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 905407
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
н 161077
17.8%
т 161077
17.8%
е 110321
12.2%
у 103304
11.4%
д 103304
11.4%
р 53444
 
5.9%
о 53444
 
5.9%
б 51652
 
5.7%
а 51652
 
5.7%
в 51652
 
5.7%
Other values (4) 4480
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 905407
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
н 161077
17.8%
т 161077
17.8%
е 110321
12.2%
у 103304
11.4%
д 103304
11.4%
р 53444
 
5.9%
о 53444
 
5.9%
б 51652
 
5.7%
а 51652
 
5.7%
в 51652
 
5.7%
Other values (4) 4480
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 905407
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
н 161077
17.8%
т 161077
17.8%
е 110321
12.2%
у 103304
11.4%
д 103304
11.4%
р 53444
 
5.9%
о 53444
 
5.9%
б 51652
 
5.7%
а 51652
 
5.7%
в 51652
 
5.7%
Other values (4) 4480
 
0.5%

max_torq
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct579
Distinct (%)0.5%
Missing50819
Missing (%)31.0%
Infinite0
Infinite (%)0.0%
Mean277.87604
Minimum19
Maximum15591
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:32.415833image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile118
Q1155
median220
Q3350
95-th percentile624
Maximum15591
Range15572
Interquartile range (IQR)195

Descriptive statistics

Standard deviation252.57462
Coefficient of variation (CV)0.90894711
Kurtosis1723.3358
Mean277.87604
Median Absolute Deviation (MAD)75
Skewness31.575202
Sum31412497
Variance63793.94
MonotonicityNot monotonic
2024-12-30T00:11:32.511920image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
250 3519
 
2.1%
350 3429
 
2.1%
210 3008
 
1.8%
155 2870
 
1.8%
160 2830
 
1.7%
150 2540
 
1.6%
400 2450
 
1.5%
200 2383
 
1.5%
300 1950
 
1.2%
145 1943
 
1.2%
Other values (569) 86123
52.6%
(Missing) 50819
31.0%
ValueCountFrequency (%)
19 2
 
< 0.1%
29 1
 
< 0.1%
42 1
 
< 0.1%
45 1
 
< 0.1%
52 33
< 0.1%
53 4
 
< 0.1%
55 4
 
< 0.1%
56 14
 
< 0.1%
57 41
< 0.1%
58 50
< 0.1%
ValueCountFrequency (%)
15591 9
 
< 0.1%
13960 4
 
< 0.1%
11500 10
 
< 0.1%
1600 7
 
< 0.1%
1430 6
 
< 0.1%
1318 1
 
< 0.1%
1280 29
< 0.1%
1231 11
 
< 0.1%
1200 4
 
< 0.1%
1150 2
 
< 0.1%

cyl_count
Real number (ℝ)

High correlation  Missing 

Distinct10
Distinct (%)< 0.1%
Missing1818
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean4.359867
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:32.588990image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q14
median4
Q34
95-th percentile6
Maximum16
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0826825
Coefficient of variation (CV)0.24832924
Kurtosis11.712784
Mean4.359867
Median Absolute Deviation (MAD)0
Skewness2.9932341
Sum706499
Variance1.1722013
MonotonicityNot monotonic
2024-12-30T00:11:32.661056image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4 131943
80.5%
6 15725
 
9.6%
8 6365
 
3.9%
3 4894
 
3.0%
5 2171
 
1.3%
12 530
 
0.3%
2 326
 
0.2%
10 79
 
< 0.1%
16 7
 
< 0.1%
1 6
 
< 0.1%
(Missing) 1818
 
1.1%
ValueCountFrequency (%)
1 6
 
< 0.1%
2 326
 
0.2%
3 4894
 
3.0%
4 131943
80.5%
5 2171
 
1.3%
6 15725
 
9.6%
8 6365
 
3.9%
10 79
 
< 0.1%
12 530
 
0.3%
16 7
 
< 0.1%
ValueCountFrequency (%)
16 7
 
< 0.1%
12 530
 
0.3%
10 79
 
< 0.1%
8 6365
 
3.9%
6 15725
 
9.6%
5 2171
 
1.3%
4 131943
80.5%
3 4894
 
3.0%
2 326
 
0.2%
1 6
 
< 0.1%

seat_count_max
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0647854
Minimum2
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:32.731120image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median5
Q35
95-th percentile7
Maximum9
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.64907982
Coefficient of variation (CV)0.12815544
Kurtosis10.753564
Mean5.0647854
Median Absolute Deviation (MAD)0
Skewness1.1664901
Sum829936
Variance0.42130461
MonotonicityNot monotonic
2024-12-30T00:11:32.803185image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 143574
87.6%
7 8549
 
5.2%
4 7418
 
4.5%
6 1553
 
0.9%
2 1473
 
0.9%
8 936
 
0.6%
9 286
 
0.2%
3 75
 
< 0.1%
ValueCountFrequency (%)
2 1473
 
0.9%
3 75
 
< 0.1%
4 7418
 
4.5%
5 143574
87.6%
6 1553
 
0.9%
7 8549
 
5.2%
8 936
 
0.6%
9 286
 
0.2%
ValueCountFrequency (%)
9 286
 
0.2%
8 936
 
0.6%
7 8549
 
5.2%
6 1553
 
0.9%
5 143574
87.6%
4 7418
 
4.5%
3 75
 
< 0.1%
2 1473
 
0.9%

seat_count_min
Real number (ℝ)

High correlation  Missing 

Distinct6
Distinct (%)0.1%
Missing158775
Missing (%)96.9%
Infinite0
Infinite (%)0.0%
Mean4.9693456
Minimum2
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:32.873249image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median5
Q35
95-th percentile6
Maximum8
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.66102436
Coefficient of variation (CV)0.1330204
Kurtosis3.3200818
Mean4.9693456
Median Absolute Deviation (MAD)0
Skewness0.70291619
Sum25289
Variance0.43695321
MonotonicityNot monotonic
2024-12-30T00:11:32.942312image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 3609
 
2.2%
4 894
 
0.5%
6 370
 
0.2%
7 196
 
0.1%
2 14
 
< 0.1%
8 6
 
< 0.1%
(Missing) 158775
96.9%
ValueCountFrequency (%)
2 14
 
< 0.1%
4 894
 
0.5%
5 3609
2.2%
6 370
 
0.2%
7 196
 
0.1%
8 6
 
< 0.1%
ValueCountFrequency (%)
8 6
 
< 0.1%
7 196
 
0.1%
6 370
 
0.2%
5 3609
2.2%
4 894
 
0.5%
2 14
 
< 0.1%

clearence_max
Real number (ℝ)

High correlation  Missing 

Distinct204
Distinct (%)0.1%
Missing3177
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean175.16148
Minimum76
Maximum460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:33.025387image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum76
5-th percentile130
Q1155
median170
Q3200
95-th percentile220
Maximum460
Range384
Interquartile range (IQR)45

Descriptive statistics

Standard deviation29.774968
Coefficient of variation (CV)0.16998583
Kurtosis0.84180774
Mean175.16148
Median Absolute Deviation (MAD)20
Skewness0.48045421
Sum28146172
Variance886.54872
MonotonicityNot monotonic
2024-12-30T00:11:33.121474image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 18266
 
11.1%
170 10758
 
6.6%
200 10656
 
6.5%
150 9909
 
6.0%
190 8146
 
5.0%
165 7501
 
4.6%
180 6501
 
4.0%
220 6414
 
3.9%
210 6322
 
3.9%
145 5696
 
3.5%
Other values (194) 70518
43.0%
ValueCountFrequency (%)
76 4
 
< 0.1%
80 16
< 0.1%
86 1
 
< 0.1%
88 2
 
< 0.1%
89 2
 
< 0.1%
90 10
< 0.1%
91 3
 
< 0.1%
93 19
< 0.1%
94 2
 
< 0.1%
95 2
 
< 0.1%
ValueCountFrequency (%)
460 1
 
< 0.1%
450 1
 
< 0.1%
433 4
< 0.1%
420 3
< 0.1%
400 2
< 0.1%
380 4
< 0.1%
361 1
 
< 0.1%
333 4
< 0.1%
326 4
< 0.1%
315 1
 
< 0.1%

v_bag_max
Real number (ℝ)

High correlation  Missing 

Distinct1060
Distinct (%)1.0%
Missing58564
Missing (%)35.7%
Infinite0
Infinite (%)0.0%
Mean613.94186
Minimum50
Maximum6700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:33.214560image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile242.9
Q1400
median490
Q3610
95-th percentile1580
Maximum6700
Range6650
Interquartile range (IQR)210

Descriptive statistics

Standard deviation453.39017
Coefficient of variation (CV)0.7384904
Kurtosis27.435767
Mean613.94186
Median Absolute Deviation (MAD)92
Skewness3.8270543
Sum64648078
Variance205562.64
MonotonicityNot monotonic
2024-12-30T00:11:33.308644image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
530 3572
 
2.2%
500 3502
 
2.1%
430 2111
 
1.3%
480 2068
 
1.3%
320 2014
 
1.2%
450 1876
 
1.1%
510 1547
 
0.9%
400 1408
 
0.9%
540 1303
 
0.8%
155 1284
 
0.8%
Other values (1050) 84615
51.6%
(Missing) 58564
35.7%
ValueCountFrequency (%)
50 8
 
< 0.1%
53 13
 
< 0.1%
57 1
 
< 0.1%
60 2
 
< 0.1%
63 2
 
< 0.1%
65 51
< 0.1%
74 9
 
< 0.1%
76 1
 
< 0.1%
80 6
 
< 0.1%
81 68
< 0.1%
ValueCountFrequency (%)
6700 21
 
< 0.1%
5800 57
< 0.1%
5400 1
 
< 0.1%
5300 49
< 0.1%
5020 2
 
< 0.1%
5010 11
 
< 0.1%
5000 1
 
< 0.1%
4630 11
 
< 0.1%
4554 16
 
< 0.1%
4200 16
 
< 0.1%

v_bag_min
Real number (ℝ)

High correlation  Missing 

Distinct741
Distinct (%)0.7%
Missing58564
Missing (%)35.7%
Infinite0
Infinite (%)0.0%
Mean508.95966
Minimum50
Maximum6700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-12-30T00:11:33.402730image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile220
Q1387
median477
Q3543
95-th percentile909
Maximum6700
Range6650
Interquartile range (IQR)156

Descriptive statistics

Standard deviation310.66755
Coefficient of variation (CV)0.61039721
Kurtosis116.03386
Mean508.95966
Median Absolute Deviation (MAD)79
Skewness8.1277469
Sum53593452
Variance96514.329
MonotonicityNot monotonic
2024-12-30T00:11:33.496815image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
530 4022
 
2.5%
500 3832
 
2.3%
430 2345
 
1.4%
320 2134
 
1.3%
480 2098
 
1.3%
450 2015
 
1.2%
510 1555
 
0.9%
400 1554
 
0.9%
540 1445
 
0.9%
408 1374
 
0.8%
Other values (731) 82926
50.6%
(Missing) 58564
35.7%
ValueCountFrequency (%)
50 8
 
< 0.1%
53 13
 
< 0.1%
57 1
 
< 0.1%
60 2
 
< 0.1%
63 3
 
< 0.1%
65 52
< 0.1%
74 9
 
< 0.1%
76 1
 
< 0.1%
80 6
 
< 0.1%
81 76
< 0.1%
ValueCountFrequency (%)
6700 21
 
< 0.1%
5800 57
< 0.1%
5400 1
 
< 0.1%
5300 49
< 0.1%
5020 2
 
< 0.1%
5000 1
 
< 0.1%
4200 4
 
< 0.1%
3424 4
 
< 0.1%
3347 2
 
< 0.1%
3300 52
< 0.1%

Interactions

2024-12-30T00:11:17.085904image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:25.463991image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:28.257528image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:30.504570image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:32.810665image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:35.099745image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:37.463893image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:43.249159image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:45.385100image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:47.608120image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:50.133415image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:52.339419image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:54.040965image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:56.650336image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:58.656158image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:00.779087image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:02.728859image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:04.346328image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:06.844598image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:08.637227image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:10.828218image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:12.394641image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:14.459518image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:17.163975image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:26.122588image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:28.333597image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:30.582641image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:32.888736image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:35.179817image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:37.739143image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:43.324228image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:45.466174image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:47.689193image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:50.213487image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:52.416488image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:54.123039image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:56.730408image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:58.735230image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:00.851152image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:02.798923image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:04.421397image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
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2024-12-30T00:10:34.778453image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:37.152611image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:42.508477image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:45.065809image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:47.287829image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:49.818128image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:52.024132image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:53.730683image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:56.334049image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:58.348879image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:00.475812image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:02.425584image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:04.057065image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:06.537319image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:08.325944image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:10.518937image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:12.121392image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:14.149236image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:16.135041image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:18.818478image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:28.017310image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:30.269356image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:32.570447image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:34.856524image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:37.234685image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:42.585547image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:45.148885image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:47.365900image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:49.900203image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:52.109210image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:53.802749image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:56.412119image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:58.430954image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:00.552882image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:02.500652image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:04.125127image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:06.608384image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:08.404016image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:10.599009image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:12.188454image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:14.227307image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:16.205104image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:18.895548image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:28.093379image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:30.344424image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:32.648518image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:34.936597image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:37.312756image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:42.790743image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:45.223954image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:47.445972image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:49.976272image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:52.188283image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:53.880820image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:56.491191image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:58.505021image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:00.629952image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:02.578722image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:04.196192image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:06.686455image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:08.481085image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:10.677081image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:12.251512image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:14.303376image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:16.282174image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:18.970616image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:28.172451image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:30.421494image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:32.725588image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:35.015669image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:37.389826image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:43.015947image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:45.303026image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:47.524044image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:50.051340image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:52.264351image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:53.957890image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:56.568261image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:10:58.578088image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:00.704019image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:02.653791image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:04.271260image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:06.761523image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:08.555153image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:10.756153image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:12.313567image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:14.381446image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-30T00:11:16.355240image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Correlations

2024-12-30T00:11:33.595905image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
accelerationavailcar_priceclass_autoclearence_maxcolorcount_ownercurb_weightcustomscyl_countdoor_countdriveeng_powereng_sizeeng_typeengine_loc1engine_loc2front_brakesfuel_brandfuel_consgross_weightheightlongmax_speedmax_torqmileageoriginal_ptspow_resrvrear_brakesseat_count_maxseat_count_minst_wheelstate_marktransmissionturbochargv_bag_maxv_bag_minv_tankwidthyear
acceleration1.0000.168-0.7000.300-0.0670.1000.166-0.5550.000-0.503-0.0440.367-0.854-0.4510.1600.2020.4650.4120.413-0.160-0.527-0.043-0.550-0.853-0.7800.2670.078-0.4860.4090.0420.3290.0650.2750.3440.384-0.180-0.226-0.392-0.594-0.419
avail0.1681.0000.0550.1050.0340.0600.2040.1110.0250.0650.0530.0360.1710.0770.0980.0590.0350.0700.0780.0510.0880.0750.1120.1370.0210.1990.1020.4830.1150.0700.1340.0720.2090.1490.1730.0290.0120.0460.1420.164
car_price-0.7000.0551.0000.0660.2860.0110.0290.7720.0000.3750.2460.0250.8180.5160.0150.1100.0380.1130.0320.1850.7570.4970.6700.6460.763-0.4970.0130.4710.1430.0830.0450.0000.0750.0210.0210.2580.2930.4160.8000.742
class_auto0.3000.1050.0661.0000.2800.1060.0860.3750.0120.3090.3550.5410.2850.4370.1340.3540.7510.1780.2300.2520.4470.3230.4810.3920.0410.0730.0500.7670.4870.2860.3490.2290.2940.3350.3120.1440.1620.3780.3710.085
clearence_max-0.0670.0340.2860.2801.0000.0650.0970.4080.0000.1840.3580.4950.1950.3500.1050.1870.4780.0990.1580.4140.3790.6960.118-0.1180.347-0.1890.092-0.1540.2040.2450.2010.0980.2130.1840.2110.3040.2960.4060.3270.220
color0.1000.0600.0110.1060.0651.0000.1260.1050.0000.0790.0800.1380.0850.0860.0530.0590.1520.0930.1010.0730.1090.1020.1060.1160.0110.0600.0980.2420.1870.0550.1210.0560.0870.1570.1440.0370.0420.0870.1230.126
count_owner0.1660.2040.0290.0860.0970.1261.0000.1760.0020.0530.1080.0920.1580.0740.1370.0260.0070.0420.0780.0460.1510.2090.1370.1250.0140.3770.4100.3070.1220.0820.1360.0670.2150.1890.1920.0460.0370.0680.2290.412
curb_weight-0.5550.1110.7720.3750.4080.1050.1761.0000.0180.5380.2910.4790.8680.7720.1960.1350.4900.0730.2050.5740.9780.6560.8080.5630.888-0.1970.1290.5060.5030.2090.1330.1120.2570.3940.4100.4030.4770.8230.9230.397
customs0.0000.0250.0000.0120.0000.0000.0020.0181.0000.0000.0000.0040.0130.0000.0370.0000.0000.0000.0000.0000.0090.0000.0230.0110.0000.0051.0000.5770.0000.0170.0000.0000.0220.0040.0050.0000.0000.0000.0090.002
cyl_count-0.5030.0650.3750.3090.1840.0790.0530.5380.0001.0000.0220.3050.5710.6640.1010.2630.4840.2380.2060.4990.5480.2140.5350.4660.5880.0580.047NaN0.3460.103-0.0080.0590.3240.2540.2010.2500.3110.6360.5120.008
door_count-0.0440.0530.2460.3550.3580.0800.1080.2910.0000.0221.0000.3340.2100.0960.0690.2930.1960.1490.1450.0770.3030.4710.0460.0710.175-0.0630.091-0.0490.2030.2530.3140.0720.1780.1640.1590.1640.0990.1010.2750.210
drive0.3670.0360.0250.5410.4950.1380.0920.4790.0040.3050.3341.0000.3850.4620.2230.1510.7450.2040.3190.3720.4810.4680.3180.3660.0120.1260.0810.8010.1800.1930.1570.0390.3180.2650.2400.1780.1990.3990.3590.301
eng_power-0.8540.1710.8180.2850.1950.0850.1580.8680.0130.5710.2100.3851.0000.6990.2010.2060.4220.2980.2530.4450.8540.4120.8160.8210.932-0.1960.0790.5590.4770.110-0.0250.0560.2350.3620.4200.2820.3420.6670.8650.450
eng_size-0.4510.0770.5160.4370.3500.0860.0740.7720.0000.6640.0960.4620.6991.0000.1550.1570.5730.1510.2460.6600.7790.4240.6590.4620.6940.0050.056NaN0.3660.1570.0450.2370.2480.3240.2870.3220.4020.8050.6750.102
eng_type0.1600.0980.0150.1340.1050.0530.1370.1960.0370.1010.0690.2230.2010.1551.0000.0860.2430.0310.5720.0870.1890.1380.1340.0920.0580.0690.0831.0000.1890.0710.1100.0790.1970.1940.3610.0760.0950.1290.1520.068
engine_loc10.2020.0590.1100.3540.1870.0590.0260.1350.0000.2630.2930.1510.2060.1570.0861.0001.0000.2030.1770.1190.1700.3680.3740.3290.1080.0480.0141.0000.2220.3510.0400.0440.2050.1110.0180.0240.0170.2120.1380.060
engine_loc20.4650.0350.0380.7510.4780.1520.0070.4900.0000.4840.1960.7450.4220.5730.2431.0001.0000.1480.3770.5360.4930.3990.3990.6091.0000.1160.0020.0000.0810.1660.1460.0140.4390.2420.2170.1720.2450.6140.3420.229
front_brakes0.4120.0700.1130.1780.0990.0930.0420.0730.0000.2380.1490.2040.2980.1510.0310.2030.1481.0000.5270.1320.0720.1950.0800.4880.0000.0800.0181.0000.5570.1420.1690.0160.4010.0840.0520.0330.0200.1000.1220.543
fuel_brand0.4130.0780.0320.2300.1580.1010.0780.2050.0000.2060.1450.3190.2530.2460.5720.1770.3770.5271.0000.1950.2450.2140.1700.3940.0370.0910.0540.0000.2710.1280.1580.0420.2960.2280.3790.0910.1090.1960.2040.312
fuel_cons-0.1600.0510.1850.2520.4140.0730.0460.5740.0000.4990.0770.3720.4450.6600.0870.1190.5360.1320.1951.0000.5240.4570.4340.0290.404-0.0020.045NaN0.0840.1230.1000.0830.1670.1480.1860.1940.2440.5800.410-0.127
gross_weight-0.5270.0880.7570.4470.3790.1090.1510.9780.0090.5480.3030.4810.8540.7790.1890.1700.4930.0720.2450.5241.0000.6610.8080.5750.870-0.1180.1080.3410.5240.2290.2070.0620.2930.4030.4160.4720.5450.8390.9180.358
height-0.0430.0750.4970.3230.6960.1020.2090.6560.0000.2140.4710.4680.4120.4240.1380.3680.3990.1950.2140.4570.6611.0000.3260.0330.396-0.2120.190-0.0160.3460.3200.3710.0980.2530.2530.2510.3420.3480.4660.5770.360
long-0.5500.1120.6700.4810.1180.1060.1370.8080.0230.5350.0460.3180.8160.6590.1340.3740.3990.0800.1700.4340.8080.3261.0000.6670.759-0.1050.0840.5310.4550.2120.0730.2290.2320.3490.3470.4400.5460.7240.8250.335
max_speed-0.8530.1370.6460.392-0.1180.1160.1250.5630.0110.4660.0710.3660.8210.4620.0920.3290.6090.4880.3940.0290.5750.0330.6671.0000.669-0.0860.0560.3140.568-0.022-0.2080.0310.3030.3650.3980.1500.2070.3880.6300.351
max_torq-0.7800.0210.7630.0410.3470.0110.0140.8880.0000.5880.1750.0120.9320.6940.0580.1081.0000.0000.0370.4040.8700.3960.7590.6691.000-0.2350.0040.4370.0030.119-0.2210.0000.0460.0140.0090.3460.4170.7080.8490.370
mileage0.2670.199-0.4970.073-0.1890.0600.377-0.1970.0050.058-0.0630.126-0.1960.0050.0690.0480.1160.0800.091-0.002-0.118-0.212-0.105-0.086-0.2351.0000.281-0.5620.0600.009-0.0350.0310.1160.1340.176-0.001-0.0100.083-0.241-0.654
original_pts0.0780.1020.0130.0500.0920.0980.4100.1291.0000.0470.0910.0810.0790.0560.0830.0140.0020.0180.0540.0450.1080.1900.0840.0560.0040.2811.0000.1260.0680.0550.1140.0230.1350.1220.1290.0360.0330.0640.1690.324
pow_resrv-0.4860.4830.4710.767-0.1540.2420.3070.5060.577NaN-0.0490.8010.559NaN1.0001.0000.0001.0000.000NaN0.341-0.0160.5310.3140.437-0.5620.1261.0000.7270.1630.7440.8260.6410.4661.0000.0560.067NaN0.4960.649
rear_brakes0.4090.1150.1430.4870.2040.1870.1220.5030.0000.3460.2030.1800.4770.3660.1890.2220.0810.5570.2710.0840.5240.3460.4550.5680.0030.0600.0680.7271.0000.1640.1540.0500.5950.4500.3580.0510.0250.2740.4860.217
seat_count_max0.0420.0700.0830.2860.2450.0550.0820.2090.0170.1030.2530.1930.1100.1570.0710.3510.1660.1420.1280.1230.2290.3200.212-0.0220.1190.0090.0550.1630.1641.0000.7260.1590.1360.1320.1280.2280.1940.2010.1880.058
seat_count_min0.3290.1340.0450.3490.2010.1210.1360.1330.000-0.0080.3140.157-0.0250.0450.1100.0400.1460.1690.1580.1000.2070.3710.073-0.208-0.221-0.0350.1140.7440.1540.7261.0000.3160.2930.1660.1860.4320.114-0.0500.0560.073
st_wheel0.0650.0720.0000.2290.0980.0560.0670.1120.0000.0590.0720.0390.0560.2370.0790.0440.0140.0160.0420.0830.0620.0980.2290.0310.0000.0310.0230.8260.0500.1590.3161.0000.4270.2460.0190.0710.0410.3050.2200.116
state_mark0.2750.2090.0750.2940.2130.0870.2150.2570.0220.3240.1780.3180.2350.2480.1970.2050.4390.4010.2960.1670.2930.2530.2320.3030.0460.1160.1350.6410.5950.1360.2930.4271.0000.4670.4480.2590.1310.2020.3070.164
transmission0.3440.1490.0210.3350.1840.1570.1890.3940.0040.2540.1640.2650.3620.3240.1940.1110.2420.0840.2280.1480.4030.2530.3490.3650.0140.1340.1220.4660.4500.1320.1660.2460.4671.0000.3940.0890.1030.2370.3870.231
turbocharg0.3840.1730.0210.3120.2110.1440.1920.4100.0050.2010.1590.2400.4200.2870.3610.0180.2170.0520.3790.1860.4160.2510.3470.3980.0090.1760.1291.0000.3580.1280.1860.0190.4480.3941.0000.1050.1350.2910.4550.268
v_bag_max-0.1800.0290.2580.1440.3040.0370.0460.4030.0000.2500.1640.1780.2820.3220.0760.0240.1720.0330.0910.1940.4720.3420.4400.1500.346-0.0010.0360.0560.0510.2280.4320.0710.2590.0890.1051.0000.8270.3840.3770.127
v_bag_min-0.2260.0120.2930.1620.2960.0420.0370.4770.0000.3110.0990.1990.3420.4020.0950.0170.2450.0200.1090.2440.5450.3480.5460.2070.417-0.0100.0330.0670.0250.1940.1140.0410.1310.1030.1350.8271.0000.4700.4570.146
v_tank-0.3920.0460.4160.3780.4060.0870.0680.8230.0000.6360.1010.3990.6670.8050.1290.2120.6140.1000.1960.5800.8390.4660.7240.3880.7080.0830.064NaN0.2740.201-0.0500.3050.2020.2370.2910.3840.4701.0000.710-0.031
width-0.5940.1420.8000.3710.3270.1230.2290.9230.0090.5120.2750.3590.8650.6750.1520.1380.3420.1220.2040.4100.9180.5770.8250.6300.849-0.2410.1690.4960.4860.1880.0560.2200.3070.3870.4550.3770.4570.7101.0000.475
year-0.4190.1640.7420.0850.2200.1260.4120.3970.0020.0080.2100.3010.4500.1020.0680.0600.2290.5430.312-0.1270.3580.3600.3350.3510.370-0.6540.3240.6490.2170.0580.0730.1160.1640.2310.2680.1270.146-0.0310.4751.000

Missing values

2024-12-30T00:11:19.217841image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-30T00:11:19.914474image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-30T00:11:21.366794image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

car_makecar_modelcar_gencar_typecar_complann_datecar_priceann_cityavailyearmileagecoloreng_sizeeng_powereng_typepow_resrvoptionstransmissiondrivest_wheelconditioncount_owneroriginal_ptscustomsstate_markclass_autodoor_countlongwidthheightv_tankcurb_weightgross_weightfront_brakesrear_brakesmax_speedaccelerationfuel_consfuel_brandengine_loc1engine_loc2turbochargmax_torqcyl_countseat_count_maxseat_count_minclearence_maxv_bag_maxv_bag_min
0Abarth500I РестайлингКабриолет1.4 MT (180 л.с.)2024-10-302700000.0ОбнинскВ наличии202044000серебристый1.4180.0БензинNaN1 опциямеханическаяпереднийЛевыйНе требует ремонта1ОригиналРастаможенИталияA2.03571.01627.01488.035.01045.0NaNдисковыедисковые225.06.74.9АИ-95переднеепоперечноетурбонаддув250.04.04.0NaN104.0185.0185.0
1Abarth500IКабриолет1.4 MT (135 л.с.)2024-03-171400000.0МинскВ наличии201355000чёрный1.4135.0БензинNaN26 опциймеханическаяпереднийЛевыйНе требует ремонта1ОригиналРастаможенИталияA3.03546.01627.01488.035.0NaNNaNдисковыедисковыеNaN6.97.1АИ-95переднеепоперечноетурбонаддув206.04.05.0NaN140.0185.0185.0
2Abarth500I РестайлингКабриолет1.4 AMT (180 л.с.)2024-09-192990000.0Санкт-ПетербургВ наличии201942500белый1.4180.0БензинNaN1 опцияроботизированнаяпереднийЛевыйНе требует ремонта1ОригиналРастаможенИталияA2.03571.01627.01488.035.01045.0NaNдисковыедисковые225.06.94.7АИ-95переднеепоперечноетурбонаддув250.04.04.0NaN104.0185.0185.0
3Abarth500IХэтчбек 3 дв.1.4 MT (135 л.с.)2024-08-152100000.0МоскваВ наличии200947800серый1.4135.0БензинNaN19 опциймеханическаяпереднийЛевыйНе требует ремонта3ОригиналРастаможенИталияA3.03546.01627.01488.070.01035.01380.0дисковыедисковые205.07.95.4АИ-98переднеепоперечноетурбонаддув206.04.04.0NaN125.0185.0185.0
4Abarth500I РестайлингХэтчбек 3 дв.1.4 AT (159 л.с.)2024-10-141650000.0МоскваВ наличии201692000чёрный1.4159.0БензинNaN16 опцийавтоматическаяпереднийЛевыйНе требует ремонта3ОригиналРастаможенИталияA3.03571.01627.01488.040.01141.0NaNдисковыедисковые211.07.87.3АИ-95переднеепоперечноетурбонаддув248.04.04.0NaN104.0268.0268.0
5Abarth500IХэтчбек 3 дв.1.4 MT (135 л.с.)2024-09-101270000.0ЗеленоградВ наличии2013153000синий1.4135.0БензинNaN21 опциямеханическаяпереднийЛевыйНе требует ремонта1ОригиналРастаможенИталияA3.03546.01627.01488.070.01035.01380.0дисковыедисковые205.07.95.4АИ-98переднеепоперечноетурбонаддув206.04.04.0NaN125.0185.0185.0
6Abarth500I РестайлингХэтчбек 3 дв.1.4 MT (180 л.с.)2024-11-021500000.0МоскваВ наличии2016151000голубой1.4180.0БензинNaN1 опциямеханическаяпереднийЛевыйНе требует ремонта1ОригиналРастаможенИталияA3.03571.01627.01488.035.01045.0NaNдисковыедисковые225.06.74.9АИ-95переднеепоперечноетурбонаддув250.04.04.0NaN104.0185.0185.0
7Abarth500I РестайлингХэтчбек 3 дв.1.4 MT (180 л.с.)2024-10-312500000.0МоскваВ наличии201844000серый1.4180.0БензинNaN34 опциимеханическаяпереднийЛевыйНе требует ремонта1ОригиналРастаможенИталияA3.03571.01627.01488.035.01045.0NaNдисковыедисковые225.06.74.9АИ-95переднеепоперечноетурбонаддув250.04.04.0NaN104.0185.0185.0
8ACCobraMk IIIРодстер7.0 MT (416 л.с.)2024-07-139000000.0ЕкатеринбургВ наличии19675000белый7.0416.0БензинNaN1 опциямеханическаязаднийЛевыйНе требует ремонта3ОригиналРастаможенВеликобританияS2.03962.01727.01219.068.0NaNNaNдисковыедисковые280.04.2NaNАИ-95переднеепродольноенет651.08.02.0NaN127.0NaNNaN
9AcuraZDX2009-2013Внедорожник 5 дв.3.7 AT (300 л.с.) 4WD2024-11-014200000.0ИркутскВ наличии201129002золотистый3.7300.0БензинNaN1 опцияавтоматическаяполныйЛевыйНе требует ремонта1ОригиналРастаможенЯпонияE5.04887.01994.01570.079.02000.0NaNдисковыеNaNNaN7.210.2АИ-95переднеепоперечноенет366.06.05.0NaN200.0745.0745.0
car_makecar_modelcar_gencar_typecar_complann_datecar_priceann_cityavailyearmileagecoloreng_sizeeng_powereng_typepow_resrvoptionstransmissiondrivest_wheelconditioncount_owneroriginal_ptscustomsstate_markclass_autodoor_countlongwidthheightv_tankcurb_weightgross_weightfront_brakesrear_brakesmax_speedaccelerationfuel_consfuel_brandengine_loc1engine_loc2turbochargmax_torqcyl_countseat_count_maxseat_count_minclearence_maxv_bag_maxv_bag_min
163854PorscheCayenneIII РестайлингВнедорожник 5 дв.3.0 AT (353 л.с.) 4WD2024-11-2213450000.0ВладивостокНа заказ20242835серый3.0353.0БензинNaN2 опцииавтоматическаяполныйЛевыйНе требует ремонта1ОригиналРастаможенГерманияE5.04930.01983.01698.075.02055.02835.0дисковыедисковые248.06.0NaNАИ-98переднеепродольноетурбонаддув500.06.05.0NaN212.01708.0772.0
163855PorscheMacanI РестайлингВнедорожник 5 дв.2.0 AMT (252 л.с.) 4WD2024-11-226150000.0ВладивостокНа заказ202145000серый2.0252.0БензинNaNMacanроботизированнаяполныйЛевыйНе требует ремонта1ОригиналРастаможенГерманияD5.04686.01923.01624.065.01870.02510.0дисковыедисковые227.06.76.6АИ-98переднеепродольноетурбонаддув370.04.05.0NaN205.01500.0500.0
163856PorscheCayenneIII РестайлингВнедорожник 5 дв.3.0 AT (353 л.с.) 4WD2024-11-2415390000.0МоскваНа заказ20246500чёрный3.0353.0БензинNaN23 опцииавтоматическаяполныйЛевыйНе требует ремонта1ОригиналРастаможенГерманияE5.04930.01983.01698.075.02055.02835.0дисковыедисковые248.06.0NaNАИ-98переднеепродольноетурбонаддув500.06.05.0NaN212.01708.0772.0
163857PorscheCayenneIII РестайлингВнедорожник 5 дв.S 4.0 AT (474 л.с.) 4WD2024-11-2319490000.0МоскваНа заказ2024100синий4.0474.0БензинNaNSавтоматическаяполныйЛевыйНе требует ремонта1ОригиналРастаможенГерманияE5.04930.01983.01698.090.02160.02930.0дисковыедисковые273.05.0NaNАИ-98переднеепродольноетурбонаддув600.08.05.0NaN210.01708.0772.0
163858PorscheCayenneIII РестайлингВнедорожник 5 дв. CoupéCoupé 3.0 AT (353 л.с.) 4WD2024-11-2218300000.0НовосибирскНа заказ20245500серебристый3.0353.0БензинNaNCoupéавтоматическаяполныйЛевыйНе требует ремонта1ОригиналРастаможенГерманияE5.04930.01983.01678.075.02085.02820.0дисковыедисковые248.05.7NaNАИ-98переднеепродольноетурбонаддув500.06.05.0NaN214.01502.0592.0
163859PorscheCayenneIII РестайлингВнедорожник 5 дв. CoupéCoupé 3.0 AT (353 л.с.) 4WD2024-11-2415850000.0МоскваНа заказ2024100чёрный3.0353.0БензинNaNCoupéавтоматическаяполныйЛевыйНе требует ремонта1ОригиналРастаможенГерманияE5.04930.01983.01678.075.02085.02820.0дисковыедисковые248.05.7NaNАИ-98переднеепродольноетурбонаддув500.06.05.0NaN214.01502.0592.0
163860PorscheCayenneIII РестайлингВнедорожник 5 дв. CoupéS Coupé 4.0 AT (474 л.с.) 4WD2024-11-2319980000.0МоскваНа заказ2024100чёрный4.0474.0БензинNaNS CoupéавтоматическаяполныйЛевыйНе требует ремонта1ОригиналРастаможенГерманияE5.04930.01983.01678.090.02190.02910.0дисковыедисковые273.04.7NaNАИ-98переднеепродольноетурбонаддув600.08.05.0NaN212.01502.0592.0
163861PorschePanameraI РестайлингЛифтбекGTS 4.8 AMT (440 л.с.) 4WD2024-01-164700000.0МоскваВ наличии201566000серый4.8440.0БензинNaNPanamera GTSроботизированнаяполныйЛевыйНе требует ремонта3ОригиналРастаможенГерманияF5.05015.01931.01418.080.01925.02500.0дисковыедисковые288.04.47.8АИ-98переднеепродольноенет520.08.05.04.0143.01263.0445.0
163862NevoA052023-н.в.Седан1.5hyb CVT (190 л.с.)2024-10-281700000.0ВладивостокНа заказ20241000серебристый1.5190.0ГибридNaN1 опциявариаторпереднийЛевыйНе требует ремонта1ОригиналРастаможенКитайD4.04785.01840.01460.048.01425.01860.0дисковыедисковые185.07.9NaNАИ-92переднеепоперечноенет330.04.05.0NaNNaNNaNNaN
163863NevoA052023-н.в.Седан1.5hyb CVT (190 л.с.)2024-10-281710000.0ВладивостокНа заказ20243600белый1.5190.0ГибридNaN1 опциявариаторпереднийЛевыйНе требует ремонта1ОригиналРастаможенКитайD4.04785.01840.01460.048.01425.01860.0дисковыедисковые185.07.9NaNАИ-92переднеепоперечноенет330.04.05.0NaNNaNNaNNaN